Normalized Calibration Of Analyte Concentration Determinations

ABSTRACT

Biosensor system measurement devices used to determine the presence and/or concentration of an analyte in a sample include normalized calibration information relating output signal or signals the device generates in response to the analyte concentration of the sample to previously determined reference sample analyte concentrations. The measurement devices use this normalized calibration information to relate one or more output signals from an electrochemical or optical analysis of a sample to the presence and/or concentration of one or more analytes in the sample. The normalized calibration information includes a normalization relationship to normalize output signals measured by the measurement device of the biosensor system and at least one normalized reference correlation relating normalized output signals to reference sample analyte concentrations.

REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.61/782,520 entitled “Calibration of Analyte ConcentrationDeterminations” filed Mar. 14, 2013, which is incorporated by referencein its entirety.

BACKGROUND

Biosensor systems provide an analysis of a biological fluid sample, suchas blood, serum, plasma, urine, saliva, interstitial, or intracellularfluid. Typically, the systems include a measurement device that analyzesa sample residing in a test sensor. The sample usually is in liquid formand in addition to being a biological fluid, may be the derivative of abiological fluid, such as an extract, a dilution, a filtrate, or areconstituted precipitate. The analysis performed by the biosensorsystem determines the presence and/or concentration of one or moreanalytes, such as alcohol, glucose, uric acid, lactate, cholesterol,bilirubin, free fatty acids, triglycerides, proteins, ketones,phenylalanine or enzymes, in the biological fluid. For example, a personwith diabetes may use a biosensor system to determine the A1c or glucoselevel in blood for adjustments to diet and/or medication.

In blood samples including hemoglobin (Hb), the presence and/orconcentration of total hemoglobin (THb) and glycated hemoglobin (HbA1c)may be determined. HbA1c (%-A1c) is a reflection of the state of glucosecontrol in diabetic patients, providing insight into the average glucosecontrol over the three months preceding the test. For diabeticindividuals, an accurate measurement of %-A1c assists in determining howwell the patient is controlling blood glucose levels with diet and/ormedication over a longer term than provided by an instantaneous measureof blood glucose level. As an instantaneous blood glucose measurementdoes not indicate blood glucose control other than when the measurementis made.

Biosensor systems may be designed to analyze one or more analytes andmay use different volumes of biological fluids. Some systems may analyzea single drop of blood, such as from 0.25-15 microliters (μL) in volume.Biosensor systems may be implemented using bench-top, portable, and likemeasurement devices. Portable measurement devices may be hand-held andallow for the identification and/or quantification of one or moreanalytes in a sample. Examples of portable measurement systems includethe Contour® meters of Bayer HealthCare in Tarrytown, N.Y., whileexamples of bench-top measurement systems include the ElectrochemicalWorkstation available from CH Instruments in Austin, Tex.

Biosensor systems may use optical and/or electrochemical methods toanalyze the biological fluid. In some optical systems, the analyteconcentration is determined by measuring light that has interacted withor been absorbed by a light-identifiable species, such as the analyte ora reaction or product formed from a chemical indicator reacting with theanalyte. In other optical systems, a chemical indicator fluoresces oremits light in response to the analyte when illuminated by an excitationbeam. The light may be converted into an electrical output signal, suchas current or potential, which may be similarly processed to the outputsignal from an electrochemical system. In either optical system, thesystem measures and correlates the light with the analyte concentrationof the sample.

In light-absorption optical systems, the chemical indicator produces areaction product that absorbs light. A chemical indicator such astetrazolium along with an enzyme such as diaphorase may be used.Tetrazolium usually forms formazan (a chromagen) in response to theredox reaction of the analyte. An incident input beam from a lightsource is directed toward the sample. The light source may be a laser, alight emitting diode, or the like. The incident beam may have awavelength selected for absorption by the reaction product. As theincident beam passes through the sample, the reaction product absorbs aportion of the incident beam, thus attenuating or reducing the intensityof the incident beam. The incident beam may be reflected back from ortransmitted through the sample to a detector. The detector collects andmeasures the attenuated incident beam (output signal). The amount oflight attenuated by the reaction product is an indication of the analyteconcentration in the sample.

In light-generated optical systems, the chemical indicator fluoresces oremits light in response to the analyte redox reaction. A detectorcollects and measures the generated light (output signal). The amount oflight produced by the chemical indicator is an indication of the analyteconcentration in the sample and is represented as a current or potentialfrom the detector.

An example of an optical system using reflectance is a laminar flow%-A1c system that determines the concentration of A1c hemoglobin inblood. These systems use immunoassay chemistry where the blood isintroduced to the test sensor of the biosensor system where it reactswith reagents and then flows along a reagent membrane. When contacted bythe blood, A1c antibody coated color beads release and move along withthe blood to a detection Zone 1. Because of the competition between theA1c in the blood sample and an A1c peptide present in detection Zone 1for the color beads, color beads not attached to the A1c antibody arecaptured at Zone 1 and are thus detected as the A1c signal from thechange in reflectance. The total hemoglobin (THb) in the blood samplealso is reacting with other blood treatment reagents and movesdownstream into detection Zone 2, where it is measured at a differentwavelength. For determining the concentration of Mc in the blood sample,the reflectance signal is proportional to the Mc analyte concentration(%-A1c), but is affected by the THb content of the blood. For the THbmeasurement, however, the reflectance in Zone 2 is inverselyproportional to the THb (mglmL) of the blood sample, but is notappreciably affected by the A1c content of the blood.

In electrochemical systems, the analyte concentration of the sample isdetermined from an electrical signal generated by an oxidation/reductionor redox reaction of the analyte or a measurable species responsive tothe analyte concentration when an input signal is applied to the sample.The input signal may be a potential or current and may be constant,variable, or a combination thereof such as when an AC signal is appliedwith a DC signal offset. The input signal may be applied as a singlepulse or in multiple pulses, sequences, or cycles. An enzyme or similarspecies may be added to the sample to enhance the electron transfer fromthe analyte during the redox reaction. The enzyme or similar species mayreact with a single analyte, thus providing specificity to a portion ofthe generated output signal. A redox mediator may be used as themeasurable species to maintain the oxidation state of the enzyme and/orassist with electron transfer from the analyte to an electrode. Thus,during the redox reaction, an enzyme or similar species may transferelectrons between the analyte and the redox mediator, while the redoxmediator transfers electrons between itself and an electrode of the testsensor.

Electrochemical biosensor systems usually include a measurement devicehaving electrical contacts that connect with the electrical conductorsof the test sensor. The conductors may be made from conductivematerials, such as solid metals, metal pastes, conductive carbon,conductive carbon pastes, conductive polymers, and the like. Theelectrical conductors connect to working and counter electrodes, and mayconnect to reference and/or other electrodes that extend into a samplereservoir depending on the design of the test sensor. One or moreelectrical conductors also may extend into the sample reservoir toprovide functionality not provided by the electrodes.

In many biosensor systems, the test sensor may be adapted for useoutside, inside, or partially inside a living organism. When usedoutside a living organism, a sample of the biological fluid may beintroduced into a sample reservoir in the test sensor. The test sensormay be placed in the measurement device before, after, or during theintroduction of the sample for analysis. When inside or partially insidea living organism, the test sensor may be continually immersed in thesample or the sample may be intermittently introduced to the testsensor. The test sensor may include a reservoir that partially isolatesa volume of the sample or be open to the sample. When open, the testsensor may take the form of a fiber or other structure placed in contactwith the biological fluid. Similarly, the sample may continuously flowthrough the test sensor, such as for continuous monitoring, or beinterrupted, such as for intermittent monitoring, for analysis.

The measurement device of an electrochemical biosensor system applies aninput signal through the electrical contacts to the electricalconductors of the test sensor. The electrical conductors convey theinput signal through the electrodes into the sample present in thesample reservoir. The redox reaction of the analyte generates anelectrical output signal in response to the input signal. The electricaloutput signal from the test sensor may be a current (as generated byamperometry or voltammetry), a potential (as generated bypotentiometry/galvanometry), or an accumulated charge (as generated bycoulometry). The measurement device may have the processing capabilityto measure and correlate the output signal with the presence and/orconcentration of one or more analytes in the sample.

In coulometry, a potential is applied to the sample to exhaustivelyoxidize or reduce the analyte. A biosensor system using coulometry isdescribed in U.S. Pat. No. 6,120,676. In amperometry, an electric signalof constant potential (voltage) is applied to the electrical conductorsof the test sensor while the measured output signal is a current.Biosensor systems using amperometry are described in U.S. Pat. Nos.5,620,579; 5,653,863; 6,153,069; and 6,413,411. In voltammetry, anelectric signal of varying potential is applied to a sample ofbiological fluid, while the measured output is current. In gatedamperometry and gated voltammetry, pulsed inputs are used as describedin WO 2007/013915 and WO 2007/040913, respectively.

Primary output signals are responsive to the analyte concentration ofthe sample and are obtained from an analytic input signal. Outputsignals that are substantially independent of signals responsive to theanalyte concentration of the sample include signals responsive totemperature and signals substantially responsive to interferents, suchas the hematocrit or acetaminophen content of a blood sample when theanalyte is glucose, for example. Output signals substantially notresponsive to analyte concentration may be referred to as secondaryoutput signals, as they are not primary output signals responsive to thealteration of light by the analyte or analyte responsive indicator, theelectrochemical redox reaction of the analyte, or the electrochemicalredox reaction of the analyte responsive redox mediator. Secondaryoutput signals are responsive to the physical or environmentalcharacteristics of the biological sample. Secondary output signals mayarise from the sample or from other sources, such as a thermocouple thatprovides an estimate of an environmental characteristic of the sample.Thus, secondary output signals may be determined from the analytic inputsignal or from another input signal.

When arising from the sample, secondary output signals may be determinedfrom the electrodes used to determine the analyte concentration of thesample, or from additional electrodes. Additional electrodes may includethe same reagent composition as the electrodes used to determine theanalyte concentration of the sample, a different reagent composition, orno reagent composition. For example, a reagent composition may be usedthat reacts with an interferent or an electrode lacking reagentcomposition may be used to study one or more physical characteristics ofthe sample, such as whole blood hematocrit.

During sample analysis, there may be more than one stimulus affectingthe primary output signal analyzed by the measurement device. Thesestimuli include the analyte concentration of the sample, the physicalcharacteristics of the sample, the environmental aspects of the sample,the manufacturing variations between test sensor lots, and the like.Since the primary goal of the analysis is to determine the presenceand/or concentration of the analyte in the sample, the analyteconcentration of the sample is referred to as the primary stimulus. Allother stimuli that affect the output signal are referred to asextraneous stimulus. Thus, the primary output signals include a majoreffect from the primary stimulus—the analyte concentration of thesample—but also include some effect from one or more extraneousstimulus. In contrast, the secondary output signals include a majoreffect from one or more extraneous stimulus, and may or may not includea major effect from the primary stimulus.

The measurement performance of a biosensor system is defined in terms ofaccuracy and precision. Accuracy reflects the combined effects ofsystematic and random error components. Systematic error, or trueness,is the difference between the average value determined from thebiosensor system and one or more accepted reference values for theanalyte concentration of the biological fluid. Trueness may be expressedin terms of mean bias, with larger mean bias values representing lowertrueness and thereby contributing to less accuracy. Precision is thecloseness of agreement among multiple analyte readings in relation to amean. One or more error in the analysis contributes to the bias and/orimprecision of the analyte concentration determined by the biosensorsystem. A reduction in the analysis error of a biosensor systemtherefore leads to an increase in accuracy and/or precision and thus animprovement in measurement performance.

Bias may be expressed in terms of “absolute bias” or “percent bias”.Absolute bias is the difference between the determined concentration andthe reference concentration, and may be expressed in the units of themeasurement, such as mg/dL, while percent bias may be expressed as apercentage of the absolute bias value over the reference concentration,or expressed as a percentage of the absolute bias over either thecut-off concentration value or the reference concentration of thesample. For example, if the cut-off concentration value is 100 mg/dL,then for glucose concentrations less than 100 mg/dL, percent bias isdefined as (the absolute bias over 100 mg/dL)*100; for glucoseconcentrations of 100 mg/dL and higher, percent bias is defined as theabsolute bias over the accepted reference value of analyteconcentration*100.

Accepted reference values for the analyte glucose in blood samples arepreferably obtained with a reference instrument, such as the YSI 2300STAT PLUS' available from YSI Inc., Yellow Springs, Ohio. Otherreference instruments and ways to determine percent bias may be used forother analytes. For the %-A1c measurements, the error may be expressedas either absolute bias or percent bias against the %-A1c referencevalue for the therapeutic range of 4-12%. Accepted reference values forthe %-A1c in blood samples may be obtained with a reference instrument,such as the Tosoh G7 instrument available from Tosoh Corp, Japan.

Hematocrit bias refers to the average difference (systematic error)between the reference glucose concentration obtained with a referenceinstrument and experimental glucose readings obtained from themeasurement device and the test sensor of a biosensor system for samplescontaining differing hematocrit levels. The difference between thereference and values obtained from the biosensor system results from thevarying hematocrit level between specific blood samples and may begenerally expressed as a percentage as follows: %Hct-Bias=100%×(G_(m)−G_(ref))/G_(ref), where G_(m) is the determinedglucose concentration at a specific hematocrit level and Gref is thereference glucose concentration at a sample hematocrit level. The largerthe absolute value of the % Hct-bias, the more the hematocrit level ofthe sample (expressed as % Hct, the percentage of red blood cellvolume/sample volume) is reducing the accuracy of the glucoseconcentration determined from the biosensor system.

For example, if different blood samples containing identical glucoseconcentrations, but having hematocrit levels of 20, 40, and 60%, areanalyzed, three different glucose concentrations will be reported by abiosensor system based on one set of calibration constants (slope andintercept of the 40% hematocrit containing blood sample, for instance).Thus, even though the glucose concentration of the different bloodsamples is the same, the system will report that the 20% hematocritsample contains more glucose than the 40% hematocrit sample, and thatthe 60% hematocrit sample contains less glucose than the 40% hematocritsample. “Hematocrit sensitivity” is an expression of the degree to whichchanges in the hematocrit level of a sample affect the bias values foran analysis performed with the biosensor system. Hematocrit sensitivitymay be defined as the numerical values of the percent biases per percenthematocrit, thus bias/%-bias per % Hct.

Biosensor systems may provide an output signal during the analysis ofthe biological fluid including error from multiple error sources. Theseerror sources contribute to the total error, which may be reflected inan abnormal output signal, such as when one or more portions or theentire output signal is non-responsive or improperly responsive to theanalyte concentration of the sample.

The total error in the output signal may originate from one or moreerror contributors, such as the physical characteristics of the sample,the environmental aspects of the sample, the operating conditions of thesystem, the manufacturing variation between test sensor lots, and thelike. Physical characteristics of the sample include hematocrit (redblood cell) concentration, interfering substances, such as lipids andproteins, and the like. Interfering substances for glucose analyses alsomay include ascorbic acid, uric acid, acetaminophen, and the like.Environmental aspects of the sample include temperature, oxygen contentof the air, and the like. Operating conditions of the system includeunderfill conditions when the sample size is not large enough,slow-filling of the test sensor by the sample, intermittent electricalcontact between the sample and one or more electrodes of the testsensor, degradation of the reagents that interact with the analyte afterthe test sensor was manufactured, and the like. Manufacturing variationsbetween test sensor lots include changes in the amount and/or activityof the reagents, changes in the electrode area and/or spacing, changesin the electrical conductivity of the conductors and electrodes, and thelike. A test sensor lot is preferably made in a single manufacturing runwhere lot-to-lot manufacturing variation is substantially reduced oreliminated. There may be other contributors or a combination of errorcontributors that cause error in the analysis.

Percent bias, mean percent bias, percent bias standard deviation (SD),percent coefficient of variance (%-CV), and hematocrit sensitivity areindependent ways to express the measurement performance of a biosensorsystem. Additional ways may be used to express the measurementperformance of a biosensor system.

Percent bias is a representation of the accuracy of the biosensor systemin relation to a reference analyte concentration, while the percent biasstandard deviation reflects the accuracy of multiple analyses, withregard to error arising from the physical characteristics of the sample,the environmental aspects of the sample, the operating conditions of thesystem, and the manufacturing variations between test sensors. Thus, adecrease in percent bias standard deviation represents an increase inthe measurement performance of the biosensor system across multipleanalyses. The percent coefficient of variance may be expressed as100%*(SD of a set of samples)/(the average of multiple readings takenfrom the same set of samples) and reflects precision of multipleanalyses. Thus, a decrease in percent bias standard deviation representsan increase in the measurement performance of the biosensor systemacross multiple analyses.

Increasing the measurement performance of the biosensor system byreducing error from these or other sources means that more of theanalyte concentrations determined by the biosensor system may be usedfor accurate therapy by the patient when blood glucose is beingmonitored, for example.

Additionally, the need to discard test sensors and repeat the analysisby the patient also may be reduced.

Biosensor systems may have a single source of uncompensated outputsignals responsive to a redox or light-based reaction of the analyte,such as the counter and working electrodes of an electrochemical system.Biosensor systems also may have the optional ability to determine orestimate temperature, such as with one or more thermocouples or othermeans. In addition to these systems, biosensor systems also may have theability to generate secondary output signals external to those from theanalyte or from a mediator responsive to the analyte. For example, in anelectrochemical test sensor, one or more electrical conductors also mayextend into the sample reservoir to provide functionality not providedby the working and counter electrodes. Such conductors may lack one ormore of the working electrode reagents, such as the mediator, thusallowing for the subtraction of a background interferent signal from theworking electrode signal.

Many biosensor systems include one or more methods to compensate forerrors associated with an analysis, thus attempting to improve themeasurement performance of the biosensor system. Compensation methodsmay increase the measurement performance of a biosensor system byproviding the biosensor system with the ability to compensate for errorin the analyses, thus increasing the accuracy and/or precision of theconcentration values obtained from the system. Conventional errorcompensation methods for physical and environmental error contributorsare traditionally developed in a laboratory as these types of errors canbe reproduced in a controlled environment.

How the measurement device of the biosensor system is calibrated in thelaboratory affects the measurement performance of the system in thehands of the user. Thus, an ongoing concern in the context ofcalibrating a measurement device is that of all the error parametersthat may affect the measurement performance of the measurement device inuse, which error parameters should be calibrated for in the laboratorybefore the measurement device is used for analyzing the analyteconcentration of samples.

Error parameters are variables, the values of which are determined fromthe analysis, such as the intermediate signals from the primary outputsignal, or from secondary output signals independent of the analyteresponsive output signal, such as thermocouples, additional electrodes,and the like. Error parameters may be any variables responsive to one ormore errors in the output signal Thus, these variables with theirdiscrete values may be the currents or potentials measured from theintermediate signals from a primary output signal, or from secondaryoutput signals, such as from thermocouple currents or voltages,additional electrode currents or voltages, and the like. Other errorparameters may be determined from these or other primary or secondaryoutput signals.

A point of diminishing returns or even poorer measurement performancemay result if the measurement device is calibrated for too many ornon-optimal error parameters. Furthermore, the more parameters that areconsidered in the calibration, the less useful the calibrationinformation stored in the measurement device may be for latercompensation of the analysis from error parameters determined during theanalysis. These calibration issues become even more complex when theanalysis being performed includes multiple output signals includinganalyte concentration-responsive (primary output signals) and/ornon-analyte responsive signals (secondary output signals). The presentinvention avoids or ameliorates at least some of the disadvantages ofmeasurement devices using conventional calibration techniques.

SUMMARY

In one aspect, the invention provides a method for determining ananalyte concentration in a sample that includes generating at least oneoutput signal from a sample; measuring at least one analyte responsiveoutput signal value from the at least one output signal, where the atleast one analyte responsive output signal value is affected by at leastone extraneous stimulus; measuring at least one extraneous stimulusresponsive output signal from the sample; determining at least onequantified extraneous stimulus value in response to the at least oneextraneous stimulus output signal; determining at least one normalizingvalue from at least one normalizing relationship; determining at leastone normalized analyte responsive output signal value in response to theat least one analyte responsive output signal value and the at least onenormalizing value; and determining at least one analyte concentration inthe sample in response to at least one normalized reference correlationand the at least one normalized analyte responsive output signal.

In another aspect of the invention, there is a method for calibrating ameasurement device of a biosensor system that includes measuring atleast two analyte responsive output signals from a sample, where theanalyte responsive output signals are affected by at least oneextraneous stimulus; determining a reference correlation between atleast one reference sample analyte concentration and the at least twoanalyte responsive output signals; measuring at least one extraneousstimulus responsive output signal from the sample; determining at leasttwo quantified extraneous stimulus values from the at least oneextraneous stimulus responsive output signal; determining a normalizingrelationship between the at least two analyte responsive output signalsand the at least two quantified extraneous stimulus values; determininga normalizing value from the normalizing relationship and the at leasttwo quantified extraneous stimulus values; determining at least twonormalized analyte responsive output signals from the at least twoanalyte responsive output signals and the normalizing value; anddetermining a normalized reference correlation between the at least twonormalized analyte responsive output signals and the least one referencesample analyte concentration.

In another aspect of the invention, there is an analyte measurementdevice that includes electrical circuitry connected to a sensorinterface, where the electrical circuitry includes a processor connectedto a signal generator and a storage medium; where the processor iscapable of measuring at least one analyte responsive output signal valuefrom a sample, where the at least one analyte responsive output signalvalue is affected by at least one extraneous stimulus; where theprocessor is capable of measuring at least one extraneous stimulusresponsive output signal from the sample; where the processor is capableof determining at least one quantified extraneous stimulus value inresponse to the at least one extraneous stimulus output signal; wherethe processor is capable of determining at least one normalizing valuefrom at least one normalizing relationship; where the processor iscapable of determining at least one normalized analyte responsive outputsignal value in response to the at least one analyte responsive outputsignal value and the at least one normalizing value; and where theprocessor is capable of determining at least one analyte concentrationin the sample in response to at least one normalized referencecorrelation and the at least one normalized analyte responsive outputsignal.

In another aspect of the invention, there is a biosensor system fordetermining an analyte concentration in a sample that includes a testsensor having a sample interface adjacent to a reservoir formed by abase, where the test sensor is capable of generating at least one outputsignal from a sample; and a measurement device having a processorconnected to a sensor interface, the sensor interface having electricalcommunication with the sample interface, and the processor havingelectrical communication with a storage medium; where the processor iscapable of measuring at least one analyte responsive output signal valuefrom the at least one output signal, where the at least one analyteresponsive output signal value is affected by at least one extraneousstimulus; where the processor is capable of measuring at least oneextraneous stimulus responsive output signal from the sample; where theprocessor is capable of determining at least one quantified extraneousstimulus value in response to the at least one extraneous stimulusoutput signal; where the processor is capable of determining at leastone normalizing value from at least one normalizing relationship; wherethe processor is capable of determining at least one normalized analyteresponsive output signal value in response to the at least one analyteresponsive output signal value and the at least one normalizing value;and where the processor is capable of determining at least one analyteconcentration in the sample in response to at least one normalizedreference correlation and the at least one normalized analyte responsiveoutput signal.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention can be better understood with reference to the followingdrawings and description. The components in the figures are notnecessarily to scale, emphasis instead being placed upon illustratingthe principles of the invention.

FIG. 1A represents a calibration method of determining calibrationinformation for incorporation into a measurement device with a reducedextraneous stimulus effect.

FIG. 1B represents an optional calibration method of also considering asecond extraneous stimulus with the calibration information.

FIG. 1C represents an analysis method of determining the analyteconcentration of a sample with a reduced extraneous stimulus effectusing a normalized reference correlation.

FIG. 2A shows the Mc reflectance signals recorded from the Zone 1detector/s of the measurement device versus reference sample analyteconcentrations (%-A1c) at four THb (total hemoglobin) concentrations (85mg/mL, 125 mg/mL, 175 mg/mL, and 230 mg/mL).

FIG. 2B shows the comparatively constant THb output signals at fourlevels of THb reference sample concentrations (85 mg/mL, 125 mg/mL, 175mg/mL, and 230 mg/mL) as determined from the Zone 2 detectors of themeasurement device.

FIG. 2C shows the individual Mc reflectance signals recorded from theZone 1 detector/s of the measurement device separated for the fourdifferent THb concentrations in blood samples.

FIG. 2D also provides an example of determining the normalizationrelationship 140, which establishes the correlation between thesynthesized extraneous stimulus responsive output signals at a singleA1c concentration and the secondary output signals responsive to thesample THb concentrations.

FIG. 2E provides an example of the determination of normalized analyteresponsive output signals from the normalizing value.

FIG. 2F provides an example of the determination of a normalizedreference correlation from combining the normalized analyte responsiveoutput signal values of FIG. 2E.

FIG. 2G provides another example of the determination of a normalizedreference correlation from the normalized analyte responsive outputsignal values of FIG. 2E.

FIG. 2H compares the normalized analyte responsive output signals to thenormalized reference correlation in the form of a normalized calibrationcurve from FIG. 2G by superimposing the normalized analyte responsiveoutput signal values on the curve.

FIG. 3A represents the normalization relationships for both channels ofan A1c measurement device having two detection channels.

FIG. 3B represents the individual normalized calibration curves for Ch1and Ch3 of the A1c measurement device.

FIG. 3C shows the normalized A1c reflectance signals from the twoindividual channels (Ch1 & Ch3) for the reference samples.

FIG. 3D shows the normalizing relationship determined by first averagingthe A1c reflectance output signals from Ch1 and Ch3 of the measurementdevice.

FIG. 3E shows the normalized reference correlation in the form of anormalized calibration curve determined for the averaged A1c reflectancesignals from Ch1 and Ch3 of the measurement device.

FIG. 4A plots the currents obtained from the measurement device versusreference sample analyte concentrations for glucose at differenttemperatures and 40% Hct.

FIG. 4B separates these primary output signal currents by thetemperature at which they were recorded.

FIG. 4C shows the correlations between the synthesized extraneousstimulus responsive output signals obtained at two separate singleglucose concentration of 100 and 500 mg/dl versus the quantifiedextraneous stimulus (temperatures) to determine the normalizationrelationships.

FIG. 4D provides an example of the determination of normalized analyteresponsive output signals from the normalizing value at 100 mg/dL.

FIG. 4E provides an example of the determination of a normalizedreference correlation from the normalized analyte responsive outputsignal values of FIG. 4D.

FIG. 4F plots the %-bias attributable to the different temperatures(6.0° C., 10.9° C., 15.9° C., 22.0° C., 30.4° C., 35.1° C., and 40.0°C.) before and after normalization.

FIG. 5A shows the output signal currents from the measurement device forreference samples including known glucose concentrations for the testedtemperatures (6.0° C., 10.9° C., 15.9° C., 22.0° C., 30.4° C., 35.1° C.,and 40.0° C.) and for the tested Hct reference sample concentrations(0%, 20%, 40%, 55%, 70%).

FIG. 5B represents the temperature normalizing relationships at 40% Hctand at two separate single glucose concentrations of 100 and 500 mg/dLin blood sample, and is the same as previously represented in FIG. 4C,as the same temperature stimulus reduction is being performed.

FIG. 5C plots the temperature normalized analyte responsive outputsignal values from FIG. 5A versus reference sample analyte concentrationfor glucose at the Hct reference sample concentrations tested in thisexample.

FIG. 5D plots the synthesized signals determined from the secondaryoutput signal responsive to the hematocrit concentration of the sampleverses temperature for reference samples including a 40% hematocritconcentration.

FIG. 5E shows the temperature normalized reference correlation for Hctwhere reference sample % Hct concentrations were plotted against thetemperature normalized Hct electrode output currents.

FIG. 5F represents the second normalizing relationship determinedbetween the resulting temperature normalized analyte responsive outputsignals and the reference sample 10-Hct values the second extraneousstimulus.

FIG. 5G graphically represents the reduction in error introduced by theextraneous stimulus of hematocrit as represented in FIG. 5C providedthrough the use of the temperature and Hct normalized analyte responsiveoutput signal values at the selected glucose concentration of 100 mg/dL.

FIG. 5H provides an example of the determination of a normalizedreference correlation from combining the temperature and hematocritnormalized analyte responsive output signal values of FIG. 5G.

FIG. 5I graphically represents the %-bias of the analyte concentrationsthat were determined by a measurement device using a conventionalreference correlation (% bias_raw), a temperature normalized referencecorrelation (% bias_T), and a temperature and Hct normalized referencecorrelation (% bias_T/Hct).

FIG. 6 depicts a schematic representation of a biosensor system thatdetermines an analyte concentration in a sample of a biological fluid.

DETAILED DESCRIPTION

Measurement devices in biosensor systems that are used to determine thepresence and/or concentration of an analyte in a sample includenormalized calibration information relating output signal or signals thedevice generates in response to the analyte concentration of the sampleto previously determined reference sample analyte concentrations. Themeasurement devices use this normalized calibration information torelate one or more output signals from an electrochemical or opticalanalysis of a sample to the presence and/or concentration of one or moreanalytes in the sample.

The present application discloses methods to determine normalizedcalibration information in a factory, laboratory or similar setting foruse in sample analysis of analyte concentration(s), methods to determinethe analyte concentration(s) of a sample using normalized calibrationinformation that is stored in a measurement device of a biosensor systemfor use during sample analysis, and a biosensor system that usesnormalized calibration information to determine the presence and/orconcentration of the analyte(s) in the sample. The normalizedcalibration information includes a normalizing relationship used todetermine normalized output signals from measured output signals and anormalized reference correlation relating the normalized output signalsdetermined during the analysis to a reference sample analyteconcentration. The normalized calibration information is useful inbiosensor systems that generate at least one primary output signalresponsive to an analyte but include or are affected by error from atleast one extraneous stimulus and that generate at least one secondaryoutput signal responsive to the at least one extraneous stimulus

During sample analysis, the primary stimulus and one or more extraneousstimulus affect the one or more output signals analyzed by themeasurement device. The primary stimulus is the analyte concentration ofthe sample. The extraneous stimulus includes all other stimuli (exceptthe analyte concentration) that affect the output signal such as thephysical characteristics of the sample, the environmental aspects of thesample, manufacturing variations between test sensor lots, and the like.The one or more output signals analyzed by the measurement device mayinclude primary output signals and secondary output signals. The primaryoutput signals include a major effect from the primary stimulus—theanalyte concentration of the sample but also include some effect fromone or more extraneous stimulus. In contrast, the secondary outputsignals include a major effect from the extraneous stimuli, and may ormay not include a major effect from the primary stimulus. Preferably,the secondary output signals are solely responsive to the one or moreextraneous stimulus.

The measurement of %-A1c in blood samples, thus the concentration ofglycated hemoglobin (%-A1c or A1c) in the total hemoglobin (THb) contentof a blood sample, may be accomplished by an immunoassay method using alaminar flow analysis. Conventionally, in the laminar flow analysis twoindependent signals are measured, primary output signals for the A1c andsecondary output signals for the THb. In this type of A1c system, theZone 1 detectors provide the primary output signal while the Zone 2detectors provide the secondary output signal. The primary outputsignals from the Zone 1 detector/s depends on the A1c concentration ofthe sample, but also on the THb concentration of the sample. Thesecondary output signals from the Zone 2 detector/s depend on the THbconcentration of the sample, but are substantially independent of theA1c concentration of the sample.

Conventional calibration in these systems focuses on establishing arelationship between the known %-A1c value of reference samples, thereflectance primary output signals determined from the Zone 1 detector/sof the measurement device responsive to A1c when these samples areanalyzed by the biosensor system, and the secondary output reflectancesignals determined from the Zone 2 detector/s of the measurement deviceresponsive to the THb content of the sample when these samples areanalyzed by the biosensor system. Thus, there are three stimuli forpotential consideration in determining the calibration information tostore in the measurement device for later use during an analysis(reference sample analyte concentration, primary output signals from themeasurement device, and secondary output signals from the measurementdevice).

In contrast to such conventional methods, a significant benefit of thepresently disclosed calibration methods in an A1c biosensor systemarises from the ability to use the laboratory determined calibrationinformation in later compensation techniques when two of the stimuli(reference sample analyte concentration (%-A1c, primary stimulus) andsecondary THb output signals (an extraneous stimulus)) are reduced toone (reference sample analyte concentration for %-A1c). One method ofaccomplishing this reduction of stimuli is through the describednormalization methods.

The normalization method reduces the dependence of the primary outputA1c signal from the measurement device on THb sample concentration bygenerating a normalized output signal from the A1c responsivereflectance values determined by the measurement device. Then anormalized reference correlation is determined between the generatednormalized output signals and the reference sample analyteconcentrations. The normalization relationship used to determine thenormalized output signals and the normalized reference correlation arestored in the measurement device. In use, the primary output signalsmeasured from the Zone 1 detector/s of the measurement device asnormalized by the described normalization procedure remain responsive toA1c, but become substantially independent of the secondary outputsignals measured from the Zone 2 detector/s responsive to the totalhemoglobin content of the sample (THb).

In a glucose analysis system, calibration generally focuses onestablishing a relationship between the known reference sample analyteconcentrations for glucose, the electrical or optical primary outputsignals determined from the sample, and the secondary output signalsresponsive to temperature and/or the hematocrit (Hct) content of thesample. These secondary output signals may be determined from athermistor, a dedicated Hct electrode, and the like, or from one or moreestimates of these values originating from the primary output signals.

In these glucose analysis systems, the electrical or optical primaryoutput signals responsive to the glucose concentration of the samplealso depends on the temperature of the sample and/or the Hct content ofthe sample. The temperature and Hct information provided by thesecondary output signals of the measurement device is substantiallyindependent of the glucose concentration of the sample. Thus, inaddition to the reference sample analyte concentration, there are atleast two (glucose and temperature responsive signals) or three(glucose, temperature, and Hct responsive signals) stimuli to considerfor determining the calibration information to store in the measurementdevice for later use during an analysis.

An example of a conventional method of addressing this calibration in aglucose system is to generate a line or curve representing the referencecorrelation between multiple reference sample analyte concentrations forglucose and the corresponding output signals of the measurement deviceat a known temperature, and/or hematocrit concentration. This referencecorrelation is stored in the measurement device for later use during theanalysis. As optical detectors convert light to a voltage and/oramperage, this process is similar for optical or electrochemicalbiosensor systems. Thus, a reference correlation may be determined forthe known reference sample analyte concentration at 20° C. and a 40%sample Hct, for example, and stored in the measurement device. Thisprocess may be repeated for multiple Hct sample concentrations at theselected temperature, for example, and these reference correlations alsostored in the measurement device. However, in this conventionaltechnique, during the analysis the measurement device must select whichof the multiple reference correlations to use or to interpolate betweenfor a specific analysis. Thus, the measurement device is attempting toselect the best reference correlation of those it has to fit the actualanalysis—a process fraught with potential issues. The goal of the lateranalysis of a sample by the biosensor system is to transform the outputsignal determined by the measurement device into the analyteconcentration of the sample using the previously determined referencecorrelation or correlations stored in the device.

In contrast to a conventional direct conversion of the output signal toa concentration by the reference correlation, a significant benefit ofthe presently disclosed calibration methods arises from the ability touse the laboratory determined calibration information stored in themeasurement device in later compensation techniques if at least two ofthese stimuli (reference sample analyte concentration for glucose andsecondary temperature output signals) are reduced to one (referencesample analyte concentration for glucose). For example, the temperaturedependence of the reference correlation on temperature may be reduced orpreferably removed through the described normalization methods to makethe reference correlation unitless. Similarly, the Hct dependence of thereference correlation also may be reduced or preferably removed throughthe described normalization methods.

The normalization method reduces the dependence of the primary outputglucose signal from the measurement device on temperature or temperatureand sample %-Hct by generating a normalized output signal from theglucose responsive current values determined by the measurement device.Then a normalized reference correlation is determined between thegenerated normalized output signals and the reference sample analyteconcentrations. The normalization relationship used to determine thenormalized output signals and the normalized reference correlation arestored in the measurement device. In use, the primary output signalsresponsive to glucose as measured by the measurement device asnormalized by the described normalization procedure remain responsive toglucose, but become substantially independent of the secondary outputsignals responsive to temperature or temperature and sample %-Hct.

For either analysis type, the normalization method may be represented bythe following expression: Normalized Output Signal=(analyte responsivesignal including the effect of one primary stimulus and at least oneextraneous stimulus)/(at least one extraneous stimulus signal) where theeffect of the primary stimulus is the effect the measurement device isattempting to detect and/or quantify. Thus, while two or more factorsaffect the outcome of the analysis, one or more factor is normalized toreduce the factors affecting the outcome of the analysis to one. Theanalyte responsive primary output signal may be the reflectance outputfrom the Zone 1 detector/s of an A1c analysis system or the currentoutput responsive to the electrochemical redox reaction of glucose or aglucose concentration responsive mediator in an electrochemical analysissystem, for example. Primary output signals from other types of analysesalso may be used with the normalization method. The extraneous stimulusmay be THb in an A1c analysis system, for example, or temperature and/orhematocrit in a glucose analysis system, for example. Thus, theextraneous stimulus arises from the secondary output signals of theanalysis.

FIG. 1A represents a calibration method 100 of determining normalizedcalibration information for incorporation into a measurement device of abiosensor system with a reduced stimulus effect. Preferably, thecalibration method 100 is performed during factory calibration of themeasurement device. The calibration method 100 also may be performed ina laboratory or similar setting. The calibration method may be performedby the measurement device, one or more analytical devices such as acomputer, or a combination of the measurement and analytical devices.

In analyte responsive output signal measurement 110, analyte responsiveoutput signals are measured from a reference sample, where the analyteresponsive output signals are affected by at least one extraneousstimulus resulting from a physical characteristic, an environmentalaspect, and/or a manufacturing variation error being incorporated intothe analyte responsive output signals. At least two analyte responsiveoutput signals are measured. Preferably, at least four, and morepreferably at least 6 analyte responsive output signals are measuredfrom the reference sample. Optical and/or electrochemical methods may beused to analyze the reference samples. FIG. 2A, as addressed furtherbelow, provides an example of the analyte responsive output signalmeasurement 110 in an A1c analysis system. FIG. 4A, as addressed furtherbelow, provides an example of the analyte responsive output signalmeasurement 110 in a glucose analysis system.

In reference correlation determination 120, a reference correlation 122is determined between one or more reference sample analyteconcentrations 124 and one or more output signals. The referencecorrelation 122 relates the reference sample analyte concentrations 124to the output signals as determined by the measurement device 126. Thereference sample analyte concentration of the reference samples may bedetermined using a reference instrument, by mixing or altering knownsample analyte concentrations, and the like.

In extraneous stimulus quantification 130, one or more extraneousstimulus responsive output signals are measured from the referencesamples and the extraneous stimulus quantified to determine at least twoquantified extraneous stimulus values 132. The stimulus responsiveoutput signals may be measured concurrently with the analyte responsiveoutput signals or at different times. Preferably, the stimulusresponsive output signals are measured concurrently with the analyteresponsive output signals. FIG. 2B, as addressed further below, providesan example of the extraneous stimulus quantification 130 in an A1canalysis system. In a glucose system, for example as represented inTable 3, when temperature is the extraneous stimulus, multiple analysesare performed at a target temperature and the actual temperature foreach analysis averaged.

The extraneous stimulus signals may be directly quantified, such as whenan optical detector or electrode outputs a specific voltage and/oramperage. The extraneous stimulus signals may be indirectly quantified,such as when a thermistor provides a specific voltage and/or amperagethat is reported as a temperature in degrees Celsius, for example. Theextraneous stimulus signals also may be indirectly quantified, such aswhen the Hct concentration of a sample is determined from a specificvoltage and/or amperage measured from an Hct electrode, for example. Theextraneous stimulus signals may be directly or indirectly quantified andthen modified to provide the quantified extraneous stimulus values 132,such as when the directly or indirectly quantified extraneous stimulusvalue is transformed into a concentration. The quantified extraneousstimulus values 132 may be determined by averaging multiple values, suchas multiple temperature readings recorded at the same targettemperature. The extraneous stimulus may be quantified through othertechniques. For example, analysis method 200, as described further belowprovides an example of extraneous stimulus quantification.

In normalizing relationship determination 140, a normalizingrelationship 142 is determined between the analyte responsive outputsignals and the quantified extraneous stimulus values 132 for theanalyte concentration of the one or more reference sample analyteconcentrations selected. Preferably, a regression technique is appliedto the analyte responsive output signals and the quantified extraneousstimulus values 132 to determine the normalizing relationship at singleselected analyte concentration. FIG. 2C, as addressed further below,provides an example of how a single analyte concentration was selectedin an A1c analysis system and used to determine synthesized extraneousstimulus responsive output signals responsive to the quantifiedextraneous stimulus signals for THb. FIG. 4B, as addressed furtherbelow, provides an example of how one of two analyte concentrations wasselected in a glucose analysis system and used to determine synthesizedextraneous stimulus responsive output signals responsive to thequantified extraneous stimulus signals for temperature. Thus, asynthesized extraneous stimulus responsive output signal was determinedfrom the primary output signals at a single selected sample analyteconcentration. The synthesized extraneous stimulus responsive outputsignal may be thought of as the extraneous stimulus responsive outputsignal extracted from the combined primary output signal from themeasurement device that includes both the primary and the extraneousstimulus. Similarly, the normalizing relationship 142 may be thought ofas a reference correlation for the extraneous stimulus.

FIG. 2D, as addressed further below, provides an example of how thenormalizing relationship determination 140 may be implemented in an A1canalysis system using the synthesized extraneous stimulus responsiveoutput signal values. An example normalizing relationship as determinedfor the A1c analysis system also is shown in FIG. 2D. FIG. 4C, asaddressed further below, provides an example of how the normalizingrelationship determination 140 may be implemented in a glucose analysissystem using the synthesized extraneous stimulus responsive primaryoutput signals. Example normalizing relationships as determined for theglucose analysis system also are shown in FIG. 4G.

Linear or non-linear (such as polynomial) regression techniques may beused to determine the normalizing relationship 142. Linear or non-linearregression techniques include those available in the M1N11AF3® version14 or version 16 statistical packages (MINTAB, INC., State College,Pa.), Microsoft Excel, or other statistical analysis packages providingregression techniques. Preferably, polynomial regression is used todetermine the normalizing relationship 142. For example in MS Excelversion 2010, the Linear Trendline Option accessible through theTrendline Layout Chart Tool may be selected to perform linearregression, while the Polynomial Trendline Option may be chosen toperform a non-linear polynomial regression. Other regression techniquesmay be used to determine the normalizing relationship 142. Thenormalizing relationship 142 is preferably stored in the measurementdevice as a portion of the calibration information.

When linear regression is used, the normalizing relationship 142 will bein the form of Y=mX+b, where m is the slope and b is the intercept ofthe regression line. When non-linear regression is used, the normalizingrelationship 142 will be in a form of Y=b₂*X²+b₁*X+b₀, and the like,where b₂, b₁ and b₀ are the coefficients of the polynomial. In both thelinear or polynomial regression equations, Y is the calculatedsynthesized extraneous stimulus responsive output signal responsive tothe extraneous stimulus portion of the primary output signal at a singleselected analyte concentration; and X is the quantified extraneousstimulus signals/values. When a value of X (the quantified extraneousstimulus signal value) is entered into either one of the relationships(linear or polynomial equations), an output value Y, representing thenormalizing value (NV) is generated from the normalizing relationship.

If a second extraneous stimulus is adversely affecting the analyteresponsive output signals and will be addressed by the calibrationinformation, the normalizing relationship determination 140 is repeatedfor a second extraneous stimulus. An example of how a second normalizingrelationship may be determined to address a second extraneous stimulusis found regarding FIG. 5, as addresses further below.

In normalizing value determination 150, a normalizing value 152 isdetermined from the normalizing relationship 142 by inputting thequantified extraneous stimulus values 132 into the normalizingrelationship 142 and solving for the normalizing value 152.

In normalized output signal determination 160, one or more normalizedanalyte responsive output signals are determined from one or moreanalyte responsive output signals and the normalizing value. Preferably,the analyte responsive output signals are divided by the normalizingvalue 152 to provide normalized analyte responsive output signals 162.This preferably reduces the effect of the extraneous stimulus on theanalyte responsive output signals. FIG. 2E, as addressed further belowprovides an example of the normalized output signal determination 160 inan A1c analysis system. FIG. 4D, as addressed further below, provides anexample of the normalized output signal determination 160 in a glucoseanalysis system at a single selected sample analyte concentration (100mg/dL).

In normalized reference correlation determination 170, a normalizedreference correlation 172 is determined between the normalized analyteresponsive output signals 162 and the reference sample analyteconcentrations 124. Preferably, a regression technique is applied to thenormalized analyte responsive output signals 162 and the referencesample analyte concentrations 124 to determine the normalized referencecorrelation 172. Linear or non-linear (such as polynomial) regressiontechniques may be used, such as those available in the MINITAB® version14 or version 16 statistical packages (MINTAB, INC., State College,Pa.), Microsoft Excel, or another statistical analysis package providingregression techniques. Preferably, polynomial regression is used todetermine the normalized reference correlation 172.

For example in MS Excel version 2010, the Linear Trendline Optionaccessible through the Trendline Layout Chart Tool may be selected toperform linear analysis, while the Polynomial Trendline Option may bechosen to perform a non-linear polynomial analysis. Other regressiontechniques may be used to determine the normalized reference correlation172. FIG. 2F, as addressed further below, provides an example of thenormalized reference correlation determination 170 in an A1c analysissystem. FIG. 2G represents the determined normalized referencecorrelation 172 expressed as a normalized calibration curve. FIG. 4E, asaddressed further below, provides an example of the normalized referencecorrelation determination 170 in a glucose analysis system.

When linear regression is used, the normalized reference correlation 172will be in the form of Y=mX+b, where m is slope and b is an intercept ofthe regression line. When non-linear regression is used, such as apolynomial, the normalized reference correlation 172 may be in a form ofY=b₂*X²+b₁*X+b₀, and the like, where b₂, b₁ and b₀ are the coefficientsof the polynomial. The normalized reference correlation 172 ispreferably stored in the measurement device as a portion of thecalibration information for later use during the analysis of a sample.

In the measurement device, Y is the normalized analyte responsive outputsignal value determined during the analysis, and X is the analyteconcentration of the sample as determined from the normalized referencecorrelation 172. As discussed further below, for the linear normalizedreference correlation, an X value (the sample analyte concentration) maybe solved for when inputting a Y value (a value of the normalized outputsignal) into the equation. For a normalized reference correlation in theform of a 2nd order polynomial, the normalized reference correlation 172may be expressed in the form of a normalized calibration curve asX=c₂*Y²+c₁*Y+c₀ where c₂, c₁ and c₀ are coefficients for the equation. Anormalized output signal input to this relationship will generate ananalyte concentration.

FIG. 1B represents an optional calibration method 102 that considers asecond extraneous stimulus with the normalized calibration information.Calibration method 102 provides normalized calibration information fromthe second extraneous stimulus for incorporation into a measurementdevice of a biosensor system with a reduced stimulus effect. Preferably,the calibration method 102 also is performed during factory calibrationof the measurement device. The calibration method 102 also may beperformed in a laboratory or similar setting. The calibration method maybe performed by the measurement device, one or more analytical devicessuch as a computer, or a combination of the measurement and analyticaldevices. Calibration method 102 combines with calibration method 100 toprovide normalized calibration information from a first extraneousstimulus and a second extraneous stimulus. Thus, FIG. 1A and FIG. 1B maybe combined when determining calibration information for the measurementdevice of the biosensor system.

If a second extraneous stimulus adversely affecting the analyteresponsive output signals is considered, such as the hematocritconcentration of the sample when the first extraneous stimulus istemperature, at least two second quantified extraneous stimulus values134 may be determined in accord with the extraneous stimulusquantification 130. FIG. 5D and FIG. 5E, as addressed further below,provide an example of the determination of second quantified extraneousstimulus values 134 in a glucose analysis system.

Then a second normalizing relationship 147 may be determined in accordwith the normalizing relationship determination 140, but where thesecond normalizing relationship 147 is determined between the normalizedanalyte responsive output signals 162 and the second quantifiedextraneous stimulus values 134 at a single selected sample analyteconcentration. The second normalizing relationship 147 is preferablystored in the measurement device as a portion of the calibrationinformation. FIG. 5F, as addressed further below, provides an example ofthe determination of a second normalizing relationship 147 in a glucoseanalysis system.

In the case of the second extraneous stimulus, a second normalizingvalue determination 155 is performed. A second normalizing value 157 isdetermined from the second normalizing relationship 147 by inputting thesecond quantified extraneous stimulus values 134 into the secondnormalizing relationship 147 and solving for the second normalizingvalue 157.

In the case of the second extraneous stimulus, a second normalizedoutput signal determination 165 is performed. Second normalized analyteresponsive output signals 167 are determined by dividing the normalizedanalyte responsive output signals 162 by the second normalizing value157. FIG. 5G, as addressed further below, provides an example ofdetermining second normalized analyte responsive output signals 167 in aglucose analysis system.

In the case of the second extraneous stimulus, a second normalizedreference correlation determination 175 is performed. A secondnormalized reference correlation 177 is determined between the secondnormalized analyte responsive output signals 167 and the referencesample analyte concentrations 124 by a regression technique, aspreviously described. FIG. 5H, as addressed further below, provides anexample of determining a second normalized reference correlation 177 ina glucose analysis system.

The second normalized reference correlation 177 is preferably stored inthe measurement device as a portion of the calibration information. Inthis case, the normalized reference correlation 172 does not need to bestored in the measurement device and is preferably not used during theanalysis. Similarly, three or more extraneous stimulus may be consideredby the calibration information, where each extraneous stimulus isrepresented by an individual normalizing relationship stored in themeasurement device in addition to a single normalized referencecorrelation prepared for the combined extraneous stimulus represented bythe individual normalizing relationships.

FIG. 1C represents an analysis method 200 of determining the analyteconcentration of a sample with a reduced extraneous stimulus effectusing a normalized reference correlation. The analysis method 200 ispreferably performed when a user activates a measurement device of abiosensor system to analyze a sample, such as blood. Preferably, thesample is blood including red blood cells. Optical and/orelectrochemical methods may be used to analyze the sample.

In analysis analyte responsive output signal measurement 220, one ormore analyte responsive output signal values 222 are measured from thesample, where the analyte responsive output signal values are affectedby one or more extraneous stimulus, such as a physical characteristic,an environmental aspect, and/or a manufacturing variation, that resultsin an error being incorporated into the analyte responsive output signalvalues. The analyte responsive output signal values 222 are measuredfrom one or more output signals generated from the sample using opticaland/or electrochemical methods, such as gated amperometry, gatedvoltammetry, or the like.

In analysis extraneous stimulus quantification 230, one or moreextraneous stimulus responsive output signals are measured. One or morequantified extraneous stimulus values are determined in response to theextraneous stimulus responsive output signals. From method 100, a singleor first quantified extraneous stimulus value 232 may be determined inresponse to a first extraneous stimulus. Methods 100 and 102 may becombined to determine a first quantified extraneous stimulus value 232in response to a first extraneous stimulus and a second quantifiedextraneous stimulus value 234 in response to a second extraneousstimulus. Other quantified extraneous stimulus values may be determined.Thus, a quantified extraneous stimulus value is determined for eachextraneous stimulus addressed by the calibration information. Thequantified extraneous stimulus responsive output signals are measuredfrom one or more output signals generated from the sample using opticaland/or electrochemical methods, such as gated amperometry, gatedvoltammetry, or the like.

In analysis normalizing value determination 250, one or more previouslydetermined normalizing relationships are used to determine one or morenormalizing values. Examples of previously determined normalizingrelationships are the previously described normalizing relationship 142and the previously described second normalizing relationship 147. Forexample, one or more of the analyte responsive output signal values 222and the quantified extraneous stimulus value 232 are input into thenormalizing relationship 142. Similarly, one or more of the analyteresponsive output signal values 222 and the quantified extraneousstimulus value 234 are input into the second normalizing relationship147. In this way, extraneous stimulus value 232 or 234 may be input tothe normalizing relationship 142 or 147 to determine the normalizingvalue. Thus, one or more analyte responsive output signal values and oneor more quantified extraneous stimulus values are input into one or morenormalizing relationships to determine one or more normalizing values.

In normalized analyte responsive output signal determination 260, atleast one normalized analyte responsive output signal value isdetermined in response to the one or more analyte responsive outputsignal values and one or more normalizing values. The one or moreanalyte responsive output signal values 222 are divided by thenormalizing value to determine one or more normalized analyte responsiveoutput signal values 262 (in the case of one external stimulus) or 267(in the case of two external stimulus). Preferably, one analyteresponsive output signal value is used to determine one normalizedanalyte responsive output signal value.

In analysis analyte concentration determination 280, one or more analyteconcentrations in the sample are determined in response to one or morenormalized reference correlations and one or more normalized analyteresponsive output signals. Preferably, a previously determinednormalized reference correlation, such as the previously describednormalized reference correlation 172 or 177, transforms the one or morenormalized analyte responsive output signal values 262 or 267 into ananalyte concentration of the sample 282. Preferably, the previouslydescribed normalized reference correlation 172 or 177 is transforms onenormalized analyte responsive output signal value into an analyteconcentration of the sample. When two or more analyte concentrations ofthe sample are determined, the analyte concentrations may be averaged toprovide an average analyte concentration of the sample.

In 290, the analyte concentration of the sample 282 may be displayed,stored for future reference, compensated, and/or used for additionalcalculations.

Thus, in the analysis method 200 the normalized reference correlation172 or 177 is incorporated into the measurement device and usedsimilarly to how a conventional reference correlation would be used totranslate primary output signal values into determined analyteconcentrations of the sample. Except that in the analysis method 200,instead of primary output signal values being transformed by thereference correlation 122, the normalized analyte responsive outputsignal value/s 262 or 267 are transformed by the normalized referencecorrelation 172 or 177, respectively, to provide the analyteconcentration of the sample with reduced effect from one or moreextraneous stimulus.

An example of the calibration method 100 for determining calibrationinformation with a reduced extraneous stimulus effect for incorporationinto the measurement device using this normalization process forcalibration is shown in FIGS. 2A-2H for an A1c analysis system. In thisexample, the sample is blood; the sample analyte is the A1c in the bloodsamples and the sample analyte concentration is the sample %-A1c. TheA1c is the primary stimulus while the THb is the extraneous stimulus.The numerical values calculated throughout FIGS. 2A-2H would bedifferent for a different analysis system or even a different A1canalysis system. This method is represented with the plots in FIG. 2Bthrough FIG. 2H.

FIG. 2A shows A1c reflectance signals recorded from the Zone 1detector/s of the measurement device versus reference sample analyteconcentrations (%-A1c) at four THb concentrations (85 mg/mL, 125 mg/mL,175 mg/mL, and 230 mg/mL). The °/0-A1c measurement was repeated twicefor each reference sample of blood including a known concentration ofA1c. Because the A1c reflectance signals measured from the Zone 1detector/s of the measurement device are THb dependent, the A1creflectance signals are spread out for the same °/°-A1c reference sampleanalyte concentration. Thus, even though the actual A1c concentrationwas identical for a set of reference samples, the measured A1creflectance signals measured from the Zone 1 detector/s for thereference sample set was different due to the THb extraneous stimulus.The equation shown in the figure represents a conventional referencecorrelation for this analysis where the output signals from themeasurement device are directly translated into analyte concentrationsby this equation. Note the relatively low R² correlation of 0.6272between the determined reference correlation (Y=0.0006x²+0.0263x+0.2239)and the output signals from the reference samples.

FIG. 2B shows the comparatively constant THb output signals at the fourTHb reference sample concentrations (85 mg/mL, 125 mg/mL, 175 mg/mL, and230 mg/mL) as determined from the Zone 2 detectors of the measurementdevice. Thus, for each THb concentration, an average signal value of theextraneous stimulus THb was determined. For example, a quantifiedextraneous stimulus signal value of ˜0.76 was determined from FIG. 1B atthe 85 mg/mL THb sample concentration by averaging. Methods other thanaveraging may be used to quantify the extraneous stimulus from thesecondary output signals.

FIG. 2C shows the individual A1c reflectance signals recorded from theZone 1 detector/s of the measurement device separated for the fourdifferent THb concentrations in blood samples. This allows a singlesample analyte concentration to be selected from which synthesizedextraneous stimulus responsive output signal values may be determinedfrom the primary output signals. In this example, linear regressionlines were determined at each of the 4 THb sample concentrations usingthe general relationship (R_(A1c)=Slope*%-A1c+Int, where R_(A1c) is theoutput signal from the measurement device, Slope and Int are the slopeand intercept, respectively of the linear regression lines at each THbsample concentration, and %-A1c is the sample analyte concentration).Other regression techniques may be used.

The regression equations determined at the 85 THb mg/mL and 230 THbmg/mL are shown on the figure, but regression equations at 127 and 175mg/mL THb also were determined. In this example, the single selectedsample analyte concentration of 9%-A1c was selected to determine thesynthesized extraneous stimulus responsive output signal values from theprimary output signals. Thus, in this example, the reference sampleanalyte concentration of 9% provided an ˜0.36 A1c synthesized extraneousstimulus responsive output signal value for the 85 mg/mL THb samplesfrom the 85 mg/mL. THb regression line and an ˜0.44 A1c synthesizedextraneous stimulus responsive output signal value for the 230 mg/mL THbsamples from the 230 mg/mL THb regression line.

Synthesized extraneous stimulus responsive output signal values can bedetermined in other ways than determining regression lines and “backdetermining” a primary output signal value from a selected referencesample analyte concentration. For example, synthesized extraneousstimulus responsive output signal values may be selected from themeasured primary output signal values at one reference sample %-A1cconcentration for all four THb levels. A single THb reflectance signalmeasured concurrently is paired with the A1c reflectance signal to formthe four pairs of Mc and THb data and to construct the plot of A1creflectance vs. THb reflectance, which will also lead to the normalizingrelationship.

While the single selected sample analyte concentration of 9%-A1c waschosen in FIG. 2C, reference sample analyte concentrations of 6 through11%-A1c also may preferably be selected. Thus, the single selectedsample analyte concentration at which to determine the synthesizedextraneous stimulus responsive output signal values is preferably nearthe middle of the range of reference sample analyte concentrations, butcan be on either side of the middle to provide the desired measurementperformance to the analysis system.

Table A, below, shows the data pairs collected from averaging the THbsignals at the same level of THb and the corresponding synthesized THbresponsive output signals calculated at a single %10-A1c concentration(in this example 9% A1c) through the above general regressionrelationship (R_(A1c)=Slope*%-A1c+Int) as in FIG. 2C.

TABLE A Synthesized and Average THb Signal Values THb (mg/mL) 85 125 175230 THb/70 (mg/mL)* 1.214 1.786 2.5 3.286 Quantified (Avg.) secondary0.7572 0.7302 0.7069 0.6796 output signal from FIG. 2B

Synthesized THb Responsive 0.3659 0.3964 0.4183 0.4400 Output SignalValues at Alc Conc. (9) from FIG. 2C 

*The samples were diluted to obtain signals appropriate for thedetectors.

FIG. 2D is a correlation plot of these four pairs of data where thesynthesized extraneous stimulus responsive output signals extracted fromthe A1c output signal in Y-axis is plotted against the THb signals(secondary output signals) in the X-axis, the extraneous stimulus. FIG.2D also provides an example of determining the normalizationrelationship 140, which establishes the correlation between thesynthesized extraneous stimulus responsive output signals at a singleA1c concentration and the secondary output signals responsive to thesample THb concentrations.

A regression technique, in this case polynomial (a₂*THb²+a₁*THb+a₀,where a₂, a₁ and a₀ are the coefficients of the 2^(nd) order polynomialnormalization function from curve-fitting and THb is the quantifiedextraneous stimulus value for THb), was then used to determine thenormalizing relationship 142 between the synthesized extraneous stimulusresponsive output signals at a single selected sample analyteconcentration and the quantified extraneous stimulus signals. Thespecific normalization relationship for the A1c analysis data from thisexample is shown in FIG. 2D as Y=−3.34X²+3.85X−0.63, thus showing thespecific 2^(nd) order polynomial coefficients for this example, where Yis the calculated synthesized extraneous stimulus responsive outputsignal responsive to the extraneous stimulus at a single selectedanalyte concentration, and X is the quantified extraneous stimulussignals/values. A different analysis would have different regressioncoefficients. When a value of X (the quantified extraneous stimulussignal value) is entered into the 2^(nd) order polynomial, a value of Yis generated through this normalizing relationship, which is thenormalizing value (NV).

FIG. 2E provides an example of the determination of normalized analyteresponsive output signals 162 using the normalizing value. Thus, the A1creflectance signals from FIG. 2C are converted to normalized primaryoutput signal values using the normalization relationship of FIG. 2D andthe normalizing value. The determination of normalized analyteresponsive output signals from the normalizing relationship and thenormalizing value may be represented by the relationshipNR_(A1c)=R_(A1c)/NV_(A1c), where NR_(A1c) are the THb normalized analyteresponsive output signals, R_(A1c) are the A1c reflectance signals fromthe measurement device, and NV_(A1c) is the normalizing value.

Preferably, the determination of normalized analyte responsive outputsignals is performed for all or the majority of the analyte responsiveoutput signal to determine the calibration information. However, asubset of the analyte responsive output signal may be used depending onthe analysis system. Thus, in FIG. 2E, the analyte responsive outputsignal values from FIG. 2C were normalized by being divided with thecorresponding normalizing values through the normalizing relationshipdetermined in FIG. 2D using the normalizing value from FIG. 2F toprovide the normalized analyte responsive output signal values shown inFIG. 2E, which are then combined into FIG. 2F.

FIG. 2F provides an example of the determination of a normalizedreference correlation from the normalized analyte responsive outputsignals of FIG. 2F. NR_(A1c) was plotted vs. the reference sampleanalyte concentrations (%-A1c) and curve-fitted by a regressiontechnique to provide the normalized reference correlation. A regressiontechnique, in this case 2nd order polynomial (a₂*%-A1c²+a1*%-A1c+a₀,where a₂, a₁, and a₀ are coefficients of the polynomial and %-A1c is theanalyte concentration of the reference samples), was used to determinethe normalized reference correlation.

The specific normalized reference correlation for the A1c analysis datafrom this example is shown in FIG. 2F as y=0.1119X²+0.0697X+0.538, thusshowing the specific 2^(nd) order polynomial coefficients for thisexample. A different analysis would have different coefficients. Alsoshown in FIG. 2F is the R²=0.9663, showing the excellent agreementbetween the normalized primary output signals and the reference sampleanalyte concentrations for %-A1c. In this determination of thenormalized reference correlation, the normalized primary output signalwas the dependent variable, while the reference sample analyteconcentrations for %-A1c was the independent variable for theregression. Thus, this normalized reference correlation may be thoughtof as outputting the normalized analyte responsive output signal valuesfrom the reference sample analyte concentrations for %-A1c.

FIG. 2G provides another example of the determination of a normalizedreference correlation from the normalized analyte responsive outputsignal values of FIG. 2E. In FIG. 2G, the normalized analyte responsiveoutput signal was the independent variable, while the reference sampleanalyte concentration for %-A1c was the dependent variable for theregression. Thus, the horizontal x and vertical y axes are reversed. Inthis example, the determined normalized reference correlation wasy=28.26X²−28.996X+0.522 and the R² correlation was 0.962, again showingthe excellent agreement between the normalized analyte responsive outputsignals and the reference sample analyte concentrations for %-A1c. Adifferent analysis would have different regression coefficients. Thus,this normalized reference correlation may be thought of as outputtingthe reference sample analyte concentrations for %-A1c from thenormalized analyte responsive output signal values.

A normalized reference correlation expressed in this way may beconsidered a “normalized calibration curve”. Such a normalizedcalibration curve providing sample analyte concentrations is preferredfor storage in the measurement device for use during the analysis 200 asthe biosensor system determines an analyte concentration from theprimary output signal, and not the reverse as would be obtained from thenormalized reference correlation of FIG. 2F. Thus, when a value of X(the normalized output signal) is entered into the 2^(nd) orderpolynomial equation, a value of Y (the analyte concentration) isobtained.

FIG. 2H compares the normalized analyte responsive output signals to thenormalized reference correlation in the form of a normalized calibrationcurve from FIG. 2G by superimposing the normalized analyte responsiveoutput signal values on the curve. Another point of interest is thecomparison of the R² correlation value of 0.6272 determined in FIG. 2Afor the conventional reference correlation and R² correlation value of0.9663 determined in FIG. 2F for the normalized reference correlation.The approximately 54% (0.9663−0.6272/0.6272*100) improvement in thenormalized reference correlation establishes the superiority of thenormalized output signal values/normalized reference correlation indetermining analyte sample concentrations in comparison to theconventional measured output signal values/reference correlation.

The resultant calibration information including the normalizingrelationship and the normalized reference correlation may be stored inthe measurement device in the form of a look-up table, one or moreequations, and the like. Other relationships also may be stored in themeasurement device. The calibration information is used by the processorof the measurement device during sample analysis to determine theanalyte concentration of the sample.

In a measurement device with one primary output signal, the abovetechniques may be used to determine calibration information for the oneprimary output signal. However, for measurement devices with more thanone primary output signal, calibration information may be determined foreach primary output signal, and the analyte concentrations determinedfrom the different calibration information combined. For example, in anA1c measurement device having more than one detector for Zone 1 withwhich to determine the primary stimulus, calibration information may bedetermined for each detector channel and then the final analyteconcentration determined by averaging the initial analyte concentrationdetermined from each detector channel.

Alternatively, for measurement devices with more than one primary outputsignal of the same primary stimulus, the output signals initially may becombined and calibration information determined for the combined signal.The independent output signals may then be transformed into analyteconcentrations using the combined calibration information to provideinitial analyte concentrations that are then combined to provide theanalyte concentration of the sample, or the combined output signals maybe transformed by the calibration information determined for thecombined signal to provide the analyte concentration of the sample. Forexample, in an A1c measurement device having more than one detector forZone 1, the output signals from both detectors may be averaged andcalibration information determined for the averaged output signals fromboth detectors. Then, the averaged output signal may be transformed intothe analyte concentration of the sample by the calibration informationdetermined from the averaged output signal or the output signal fromboth channels transformed by the calibration information determined fromthe averaged output signal to provide two initial analyteconcentrations, which are then averaged to provide the final analyteconcentration of the sample.

The examples described with regard to FIGS. 3A-3E show the benefit ofdetermining independent calibration information for each of twoindividual detector channels of a measurement device, where each of thetwo output signal channels includes both A1c and THb responsiveinformation. The numerical values calculated throughout FIGS. 3A-3Ewould be different for a different analysis system or even a differentA1c analysis system.

FIG. 3A represents the normalization relationships for both channels ofan A1c measurement device having two detection channels. Thus, for thisexample, FIG. 3A shows the two separate normalizing relationships forthe primary output signals from the Zone 1 channel one detector (Ch1)and the secondary output signals from the Zone 2 channel two detector(Ch2); and for the primary output signals from the Zone 1 channel threedetector (Ch3) and the secondary output signals from the Zone 2 channelfour detector (Ch4). Thus, Ch1 and Ch3 provide A1c responsive outputsignals, while Ch2 and Ch4 provide THb responsive output signals.

FIG. 3B represents the individual normalized calibration curves for Ch1and Ch3 of the A1c measurement device. Also plotted on the figure arethe normalized output signals determined from Ch1/Ch2, the normalizedoutput signals determined from Ch3/Ch4, and the average of thesenormalized output signals. A polynomial regression technique was used todetermine the normalized reference correlations in the form ofnormalized calibration curves, as previously described.

FIG. 3C shows the normalized A1c reflectance signals from the twoindividual channels (Ch1 & Ch3) for the reference samples. Averaging maybe performed for the two initial %-A1c concentrations to provide thefinal A1c concentration. The mean and percent bias standard deviation(SD) values in Table 1 and in Table 2 below show the separate individualchannels results, as well as the average A1c results for the calibrationand measurement device sample analyses, respectively. For both thecalibration, using reference samples, and analyses, performed with themeasurement device using the calibration information, the averageddetermined analyte concentrations are improved over the individualchannel results. From Table 2, the Ch1 bias standard deviation wasreduced by nearly nine percent (8.8%) (5.58−5.09/5.58*100), while theCh2 bias standard deviation was reduced by over 18% (18.3%)(6.23−5.09/6.23*100) for the average. Thus, the bias standard deviationwas reduced by an average of greater than 10% (13.5%) (8.8+18.3/2) forthe averaged determined analyte concentrations.

TABLE 1 Calibration %-bias1 %-bias3 %-bias_Avg Mean 0.274 0.295 0.156 SD5.25 5.52 3.97

TABLE 2 Analysis %-bias1 %-bias3 %-bias_Avg Mean 0.055 0.184 0.120 SD5.58 6.23 5.09

In addition to determining calibration information for each channel, andthen combining the intermediate sample concentrations determined foreach channel, the output signals from each channel may be first combinedand then used to determine calibration information for the combinedsignal. FIG. 3D shows the normalizing relationship determined by firstaveraging the A1c reflectance output signals from Ch1 and Ch3 of themeasurement device. Thus, the same output signals used to determine thetwo normalizing relationships represented in FIG. 3A were firstaveraged. FIG. 3E shows the normalized reference correlation in the formof a normalized calibration curve determined for the averaged A1creflectance signals from Ch1 and Ch3 of the measurement device. Outputsignals from each channel of the measurement device can then beconverted to initial analyte concentrations with this calibrationinformation and the initial analyte concentrations averaged to determinea final sample analyte concentration.

An example of the calibration method 100 for determining calibrationinformation with a reduced extraneous stimulus effect for temperature onthe primary output signal from an electrochemical glucose analysissystem is shown in FIGS. 4A-4F. In this example, the sample is the bloodand the sample analyte is the glucose (the primary stimulus). The sampleanalyte concentration is the sample glucose concentration and theextraneous stimulus are temperature and hematocrit.

For the temperature effect, the currents measured by the measurementdevice from reference samples at 40% Hct (the %-Hct at which theconventional reference correlation relating output currents andreference sample analyte concentrations was determined) and at differenttemperatures were normalized. In this type of glucose system, theworking and counter electrodes provide the primary output signal whilethe temperature sensor provides the secondary output signal. Thisprocess is represented with the plots in FIG. 4A through FIG. 4F. Thenumerical values calculated throughout FIGS. 4A-4F would be differentfor a different analysis system or even a different glucose analysissystem.

FIG. 4A plots the currents obtained from the measurement device versusreference sample analyte concentrations for glucose at differenttemperatures and 40% Hct. The currents become more widely spread due tochanges in temperature at higher sample glucose concentrations. Whilethe currents determined by the measurement device from reference samplesincluding ˜80 mg/dL of glucose in blood were closely grouped around ˜75nA, the currents determined by the measurement device from referencesamples including ˜320 mg/dL and ˜580 mg/dL of glucose were widelyspaced. In fact, as shown in FIG. 4A a linear regression line determinedfrom these currents showed an R² correlation of only 0.6. This figuremay be thought of as showing the effect of an extraneous stimulus onanalyte responsive output signals as previously observed in FIG. 2C. InFIG. 2C the extraneous stimulus was sample THb, while in FIG. 4A it wastemperature.

FIG. 4B separates these primary output signal currents by thetemperature at which they were recorded. Thus, temperature is theextraneous stimulus that adversely affects the analyte responsive outputsignal currents from the measurement device, even though the analyteconcentrations of the reference samples are identical at eachtemperature. In this example, linear regression lines were determined ateach of the 5 temperatures using the general relationshipi_(G)=Slope*G_(Ref)+Int, where i_(G) is the glucose responsive currentfrom the measurement device and G_(Ref) is the reference sample analyteconcentration for glucose from which to determine the synthesizedextraneous stimulus responsive output signal values. Other techniquesmay be used to determine the synthesized extraneous stimulus responsiveoutput signal values.

In FIG. 4B, two sample glucose concentrations of 100 and 500 mg/dL wereselected at which to determine the synthesized extraneous stimulusresponsive output signal values at their corresponding temperatures.Thus, unlike in the A1c system where the single selected sample analyteconcentration of 9% was selected, this example shows that synthesizedextraneous stimulus responsive output signal values may be determined atmore than one single selected sample analyte concentration. Singleselected sample analyte concentrations other than 100 and 500 mg/dL maybe used.

Table 3, below, provides the synthesized extraneous stimulus responsiveoutput signal values obtained for each temperature at 100 and 500 mg/dLglucose sample analyte concentration from the individual regressionlines by using the regression equations similar to that previouslydescribed with regard to the A1c example (i_(G)=Slope*G_(Ref)+Int). Thetemperatures values in the table are averages from all the measuredtemperatures when performing the analysis of the reference samples atthe target temperatures. Thus, Table 3 forms two sets of pairs at theselected single glucose concentration of 100 or 500 mg/dL, each setcontaining seven pairs of output signals-temperatures data.

TABLE 3 Synthesized Output Signal Values Avg. Temp, C. 6.0 10.9 15.922.0 30.4 35.1 40.0 500 mg/dL 205.78 283.53 373.96 462.61 639.89 705.54809.11 100 mg/dL 41.16 56.71 74.79 92.52 127.98 141.11 161.82Thus, FIG. 4B separates the output currents from the measurement deviceby temperature, as opposed to THb sample concentration as described inFIG. 2C. Only five correlation lines were plotted for five of the seventemperatures tested, in order not to crowd the plot.

FIG. 4C shows the correlations between the synthesized extraneousstimulus responsive output signals obtained at two separate singleglucose concentration of 100 and 500 mg/dl versus the quantifiedextraneous stimulus (temperatures) to determine the normalizationrelationships. FIG. 4C also provides an example of determining thenormalization relationship 140, which considers temperature fornormalization of the analyte responsive output signals. The verticalY-axis of FIG. 4C shows the extraneous stimulus responsive values assynthesized from the regression lines of FIG. 4B and determined at thesingle selected sample analyte concentrations of 100 and 500 mg/dLglucose for the five temperatures. The horizontal X-axis of FIG. 4Cshows the average value determined at each target temperature for theextraneous stimulus temperature.

A regression technique, in this case polynomial (a₂*T²+a₁*T+a₀, wherea₂, a₁ and a₀ are the coefficients of the 2^(nd) order polynomialnormalization function from curve-fitting and T is the quantifiedextraneous stimulus value for temperature), was then used to determinethe normalizing relationship 142 between the synthesized extraneousstimulus responsive output signals at a single selected sample analyteconcentration and the quantified extraneous stimulus signals. Thespecific normalization relationship for the 100 and 500 mg/dL glucoseanalysis data from this example is shown in FIG. 4C asy=0.0104X²+3.0646x+22.366 (100 mg/dL) and y=0.05214X²+15.3228x+111.832(500 mg/dL), thus showing the specific 2^(nd) order polynomialcoefficients for this example, where Y is the calculated synthesizedextraneous responsive output signal responsive to the extraneousstimulus signal at a single selected analyte concentration, and X is thequantified extraneous stimulus signals/values. A different analysiswould have different coefficients. When a value of X (the quantifiedextraneous stimulus signal value) is entered into the 2^(nd) orderpolynomial, a value of Y is generated through this normalizingrelationship, which is the normalizing value (NV). This figure may bethought of similarly as to FIG. 2D; however, in this example,normalizing values may be determined at two reference sample analyteconcentrations.

FIG. 4D provides an example of the determination of normalized analyteresponsive output signals 162 from the normalizing value at 100 mg/dL.Thus, the analyte responsive output signals from the vertical y-axis ofFIG. 4B were converted to normalized primary output signal values bydividing the analyte responsive output signals with their correspondingnormalizing values obtained from the normalization relationship of FIG.4C. The determination of the normalized analyte responsive outputsignals from the normalizing relationship and the normalizing value maybe represented by the relationship N_(iG)=i_(measured)/NIV_(Temp), whereN_(iG), are the temperature normalized analyte responsive outputsignals, i_(measured) are the glucose responsive currents from themeasurement device, and NV_(Temp) is the normalizing value determinedfrom temperature normalization. Thus, the five individual lines of FIG.4B for the different temperatures collapsed into a group of closelypacked lines as represented in FIG. 4D. As previously discussed,preferably, the determination of normalized analyte responsive outputsignals is performed for all or the majority of the analyte responsiveoutput signal to determine the calibration information.

FIG. 4E provides an example of the determination of a normalizedreference correlation from the normalized analyte responsive outputsignal values of FIG. 4D. N_(iG) was plotted vs. the reference sampleanalyte concentrations and curve-fitted by a regression technique toprovide the normalized reference correlation. A regression technique, inthis case linear (Y=Slope*X+Int), was used to determine the normalizedreference correlation, where during the analysis 200 Y is the normalizedprimary output signal determined by the measurement device and X is thereference analyte concentration of the sample. Thus, when a Y value (thenormalized output signal) is entered into the linear regressionequation, an X value (the analyte concentration) is obtained by solvingthe equation.

The specific normalized reference correlation for the glucose analysisdata from this example is shown in FIG. 4E as Y=0.01033X−0.14082, thusshowing the specific linear coefficients for this example. A differentanalysis would have different coefficients. Also shown in FIG. 4E is theR²=0.9946, showing the excellent agreement between the normalizedprimary output signals and the reference sample analyte concentrations.Thus, this normalized reference correlation may be thought of asproviding determined sample analyte concentrations for glucose from thenormalized analyte responsive output signal values.

For the temperatures measured (6.0° C., 10.9° C., 15.9° C., 22.0° C.,30.4° C., 35.1° C., and 40.0° C.), the average temperatures, theregression slope obtained at each temperature using a conventionalreference correlation, and the regression slope obtained at eachtemperature using the normalized reference correlation at the 100 mg/dLglucose reference sample analyte concentration are tabulated in Table 4,below.

TABLE 4 Summary of Normalization for Temperature Stimulus ConventionalNormalized Reference Reference Temp ° C. Correlation Slopes CorrelationSlopes  6.0 0.412 0.0103 10.9 0.567 0.0099 15.9 0.748 0.0101 22.0 0.9250.0097 30.4 1.280 0.0102 35.1 1.411 0.0099 40.0 1.618 0.0100 Mean slope0.9944 0.0100147 SD, slope 0.4532 0.00023014 %-CV 45.6 2.3

The improvement in measurement performance may be better appreciated bylooking at the mean response slopes and the %-CV of the slopes beforeand after normalization. The %-CV of the response slopes determined withprimary output signals from the measurement device and a conventionalreference correlation was 45.6%. In contrast, the %-CV of the responseslopes determined with the described normalized primary output signalsand normalized reference correlation was reduced to 2.3%, an approximate95% reduction (45.6−2.3/45.6*100). This reduction is graphicallyrepresented when the normalized output signal currents are transformedinto sample analyte concentrations with the normalized referencecorrelation of FIG. 4E in comparison to when the underlying currentsfrom the measurement device are transformed by the conventionalreference correlation of FIG. 4A.

FIG. 4F plots the %-bias of the determined glucose concentrationsattributable to the different temperatures (6.0° C., 10.9° C., 15.9° C.,22.0° C., 30.4° C., 35.1° C., and 40.0° C.) before and afternormalization. The measured currents showed a %-bias spread ofapproximately ±60 when transformed by the conventional referencecorrelation, while the normalized currents showed a %-bias spread ofapproximately ±20. Thus, an approximate 3× reduction in %-bias would beexpected for sample analyte concentrations determined with a biosensorsystem including a measurement device including calibration informationin accord with the present method in comparison to sample analyteconcentrations determined with a measurement device includingcalibration information provided by a conventional method lackingnormalization reduction of the temperature stimulus.

Once the effect of temperature is reduced, the effect of a secondextraneous stimulus, such as sample hematocrit also may be substantiallyreduced using a two-step normalization process, thus the combination ofFIG. 1A and FIG. 1B. In this type of glucose system, the working andcounter electrodes provide the primary output signal while a temperaturesensor preferably provides a secondary output signal and a hematocritelectrode preferably provides an additional secondary output signal. Thesecondary output signals responsive to temperature and hematocrit mayarise in other ways, as previously discussed. The step-wisenormalization by temperature and then by sample hematocrit was generallyperformed by normalizing the output currents from the measurement deviceat multiple temperatures (as described above) and then normalizing theresulting temperature normalized output signal currents for multiple Hctsample concentrations using a hematocrit normalization relationship.

An example of the calibration method 102 for determining calibrationinformation with a reduced secondary extraneous stimulus effect forhematocrit on the primary output signal from an electrochemical glucoseanalysis system is shown in FIGS. 5A-5I. In this example, the sampleanalyte is glucose (the primary stimulus) and the sample analyteconcentration is the glucose concentration in the sample of blood.Hematocrit concentration in the blood sample is the second extraneousstimulus in addition to the first extraneous stimulus, temperature. Thismethod is represented with the plots in FIG. 5A through FIG. 5I. Thenumerical values calculated throughout FIGS. 5A-5I would be differentfor a different analysis system or even a different glucose analysissystem.

FIG. 5A shows the output signal currents from the measurement device forreference samples including known glucose concentrations for the testedtemperatures (6.0° C., 10.9° C., 15.9° C., 22.0° C., 30.4° C., 35.1° C.,and 40.0° C.) and for the tested Hct reference sample concentrations(0%, 20%, 40%, 55%, 70%). The secondary output signals obtained from ahematocrit electrode were used to obtain the Hct responsive outputcurrents from the reference samples having known hematocritconcentrations. As expected, the currents determined by the measurementdevice from reference samples including ˜80 mg/dL glucose analyteconcentration were closely grouped around ˜75 nA, while the currentsdetermined by the measurement device from reference samples including˜320 mg/dL and ˜580 mg/dL glucose analyte concentrations were widelyspaced.

FIG. 5B represents the temperature normalizing relationships at 40% Hctand at two separate single glucose concentrations of 100 and 500 mg/dLin blood sample, and is the same as previously represented in FIG. 4C,as the same temperature stimulus reduction is being performed.Temperature normalization was the first step taken in this example toreduce the effect of extraneous stimulus from temperature, as the sametemperature stimulus reduction was performed. From FIG. 5B, normalizedanalyte responsive output signals with a reduction in the effect of thetemperature stimulus were determined as previously described.

FIG. 5C plots the temperature normalized analyte responsive outputsignal values from FIG. 5A versus reference sample analyte concentrationfor glucose at the Hct reference sample concentrations tested in thisexample. As expected, significant spread in the normalized currents isobserved at higher glucose concentrations for the different Hctconcentrations, even though the underlying sample analyte concentrationis the same. FIG. 5C may be thought of as being similar to FIG. 4B, butshowing the effect of the second extraneous stimulus, hematocrit asopposed to temperature.

Temperature as an error parameter or an extraneous stimulus ispreferably measured concurrently with the primary stimulus and its valueis independent of other factors. The hematocrit concentration in theblood samples is provided along with the glucose concentration, but thehematocrit responsive secondary output signals aretemperature-dependent. Therefore, temperature is also an extraneousstimulus for the Hct responsive output signals and the effect oftemperature is preferably reduced by normalization. The procedureinvolved is first to construct the Hct responsive currents plot againstthe temperature at a single selected Hct concentration. Then, a Hctnormalized calibration curve is determined that provides the calculated%-Hct values from the temperature normalized Hct output signals forlater use.

Synthesized Hct output signal values were generated similarly aspreviously described for A1c and glucose with regard to FIG. 2C and FIG.4B, respectively. In this determination, which is not shown in a figure,the current recorded from the Hct electrode for the reference samples ateach reference sample hematocrit concentration was plotted on thevertical y-axis while the known reference sample hematocritconcentrations were plotted on the horizontal X-axis. A regression linewas plotted for each temperature (6.0° C., 10.9° C., 15.9° C., 22.0° C.,30.4° C., 35.1° C., and 40.0° C.) and a synthesized Hct output signalvalue was determined at a 40% sample hematocrit concentration for eachtemperature.

FIG. 5D plots the synthesized signals determined from the secondaryoutput signal responsive to the hematocrit concentration of the sampleverses temperature for reference samples including a 40% hematocritconcentration. From the above operation, seven pairs of the synthesizedHct output signals from a single selected Hct concentration of 40% andthe temperatures, at which the reference samples were analyzed, wereobtained and plotted. A regression technique, in this instancepolynomial, was then used to determine a specific normalizingrelationship for Hct as shown in the figure, but having the general formNV_(Hct)=(b₂*T²+b₁*T+b₀, where b₂, b₁ and b₀ are the coefficients of the2^(nd) order polynomial normalization function from curve-fitting and Tis the temperature). A Hct normalizing value was determined, andnormalized Hct electrode currents were determined with the generalrelationship Ni_ _(Hct) =i_ _(Hct) /NV_(Hct), where Ni_ _(Hct) are thetemperature normalized Hct electrode currents, i_ _(Hct) are the Hctresponsive currents from the Hct electrode, and NV_(Hct) is the Hctnormalizing value.

FIG. 5E shows the temperature normalized reference correlation for Hctwhere reference sample % Hct concentrations were plotted against thetemperature normalized Hct electrode output currents. Other interferentsmay be similarly treated if the biosensor system provides a secondaryoutput signal responsive to the interferent.

FIG. 5F represents the second normalizing relationship determinedbetween the resulting temperature normalized analyte responsive outputsignals and the reference sample %-Hct values—the second extraneousstimulus. FIG. 5F established the correlation between the temperaturenormalized output signals and the sample %-Hct concentrations. That is,at the single selected glucose concentration of either 100 or 500 mg/dL,the temperature normalized output signals are substantively responsiveto the Hct concentration, the second extraneous stimulus.

A regression technique, in this case polynomial (c₂*Hct2+c₁*Hct+c₀,where c₂, c₁ and c₀ are the coefficients of the 2^(nd) order polynomialnormalization function from curve-fitting and Hct is the secondquantified extraneous stimulus value for Hct), was then used todetermine the normalizing relationship between the synthesized secondextraneous stimulus responsive output signals at a single selectedsample analyte concentration and the quantified second extraneousstimulus values (reference sample hematocrit concentrations of 0%, 20%,40%, 55%, and 70%. The specific normalization relationship for the 100and 500 mg/dL glucose analysis data from this example is shown in FIG.5F as y=−0.00008X²−0.00456X+1.31152 (100 mg/dL) andy=−0.0004X²−0.0228X+6.5577 (500 mg/dL), thus showing the specific 2^(nd)order polynomial coefficients for this example, where Y is thecalculated synthesized second extraneous stimulus responsive outputsignal responsive to the second extraneous stimulus (Hct) at a singleselected analyte concentration, and X is the quantified secondextraneous stimulus values. A different analysis would have differentcoefficients. When a value of X (the quantified second extraneousstimulus signal value) is entered into the 2^(nd) order polynomial, avalue of Y is generated through this normalizing relationship, which isthe normalizing value (NV).

Second normalized analyte responsive output signals 167 were thendetermined from the normalizing value at 100 mg/dL. Thus, thetemperature normalized output signals from the vertical Y-axis of FIG.5C were then converted to second normalized primary signal values usingthe normalization relationship of FIG. 5F with their correspondingnormalizing values. The determination of the normalized analyteresponsive signals from the normalizing value may be represented by therelationship Ni_(G)=i_(measured)/NV_(Temp-Hct), where Ni_(G) are thetemperature and hematocrit normalized analyte responsive signals,imeasured are the glucose responsive currents from the measurementdevice, and NV_(Temp-Hct) is the normalizing value determined fromtemperature and hematocrit normalization.

FIG. 5G graphically represents the reduction in error introduced by theextraneous stimulus of hematocrit as represented in FIG. 5C providedthrough the use of the temperature and Hct normalized analyte responsiveoutput signal values at the selected glucose concentration of 100 mg/dL.Thus, this figure may be thought of similarly as to FIG. 4C; however, inthis example, both temperature and hematocrit normalized currents areused. Regression equations are shown for the lower (0%) and upper (70%)limit Hct concentrations. In relation to FIG. 5C, the divergence betweenthe upper and lower Hct limits has been reduced from approximately 4normalized output signal units (FIG. 5C) to approximately 0.25 outputsignal units (FIG. 5G) at ˜600 mg/dL, an approximate 93% reduction(4−0.25/4*100) in divergence between the regression lines.

FIG. 5H provides an example of the determination of a normalizedreference correlation from combining the temperature and hematocritnormalized analyte responsive output signal values of FIG. 5G. Thetemperature and hematocrit normalized signals were plotted on thevertical Y-axis versus the reference sample analyte concentrations forglucose and curve-fitted by a regression technique to provide thenormalized reference correlation. A regression technique, in this caselinear (Y=Slope*X+Int), was used to determine the normalized referencecorrelation, where during the analysis 200 Y is a normalized primaryoutput signal value determined by the measurement device and X is thedetermined analyte concentration of the sample.

The specific normalized reference correlation for the glucose analysisdata from this example is shown in FIG. 5H as Y=0.0104X−0.1339, thusshowing the specific linear coefficients for this example. A differentanalysis would have different coefficients. Also shown in FIG. 5H is theR²=0.9828, showing the excellent agreement between the normalizedprimary output signals and the reference sample analyte concentrationsfor glucose. Thus, the normalized reference correlation relates thenormalized output signals and the sample analyte concentration. When anormalized output signal is input into the normalized referencecorrelation, a sample analyte concentration is generated. FIG. 5H may bethought of as being similar to FIG. 4E except incorporating bothtemperature and Hct normalization into the calibration information.

For the hematocrit concentrations provided from the blood samples (0%,20%, 40%, 55%, and 70%), the slopes of the reference correlations withtemperature and temperature/hematocrit normalizations are tabulated inTable 5, where Slope/T is the slope from the temperature normalizedreference correlation and Slope/T/H is the slope from the temperatureand hematocrit normalized reference correlation.

TABLE 5 Summary of Normalization for Temperature and Hematocrit StimuliSlope/T Slope/T/H % Hct FIG. 5C FIG. 5G  0 0.0133 0.0105 20 0.01260.0107 40 0.0105 0.0105 55 0.0082 0.0101 70 0.0058 0.0102 Mean slope0.0101 0.0104 SD, slope 0.0031 0.0002 % CV 30.7 2.3

The improvement in measurement performance may be better appreciated bylooking at the mean response slopes and the %-CV of the slopes fortemperature normalization alone and after normalization for bothtemperature and hematocrit. In this example, the %-CV of the responseslopes determined with a temperature normalized reference correlationwas 30.7%. In contrast, the %-CV of the response slopes determined withthe described temperature and hematocrit normalized referencecorrelation was reduced to 2.3%, an approximate 92% reduction(30.7−2.3/30.7*100) in %-CV providing a substantial increase inmeasurement performance to the biosensor system.

FIG. 5I graphically represents the %-bias of the analyte (glucose)concentrations determined by the measurement device of a biosensorsystem using a conventional reference correlation (% bias_raw), atemperature normalized reference correlation (% bias_T), and atemperature and Hct normalized reference correlation (% bias_T/Hct). Thefigure establishes that the output currents including the temperatureand hematocrit stimuli in combination show a %-bias of nearly ±100% whendirectly transformed with a conventional reference correlation intosample analyte concentrations. Removal of the temperature stimulus fromthe calibration information allows the measurement device to determineanalyte concentrations with a %-bias of approximately ±50%, whilefurther removal of the Hct stimulus reduces the %-bias to approximately±30%. Thus, an approximately 70% reduction in %-bias was observed withthe described normalized calibration information in relation toconventional calibration information. This is a substantial improvementin relation to conventional systems with regard to the measurementperformance the biosensor system may provide from the calibrationinformation without additional compensation.

FIG. 6 depicts a schematic representation of a biosensor system 500 thatdetermines an analyte concentration in a sample of a biological fluid.Biosensor system 500 includes a measurement device 502 and a test sensor504. The measurement device 502 may be implemented in an analyticalinstrument, including a bench-top device, a portable or hand-helddevice, or the like. Preferably the measurement device 502 isimplemented in a hand-held device. The measurement device 502 and thetest sensor 504 may be adapted to implement an electrochemical sensorsystem, an optical sensor system, a combination thereof, or the like.

The biosensor system 500 determines the analyte concentration of thesample using the calibration information developed in accord with thepreviously described normalization techniques and stored in themeasurement device 502. The calibration information from one or both ofthe calibration methods 100 and 102 may be stored in the measurementdevice 502. The calibration information includes one or more normalizingrelationships and one or more normalized reference correlations. One orboth calibration methods 100 and 102 may be stored in the measurementdevice 502 so the normalized calibration information may be determinedby the measurement device 502. The analysis method 200 may be stored inthe measurement device for implementation by the biosensor system 500.The method of measurement device calibration may improve the measurementperformance of the biosensor system 500 in determining the analyteconcentration of the sample. The biosensor system 500 may be utilized todetermine analyte concentrations, including those of glucose, A1c, uricacid, lactate, cholesterol, bilirubin, and the like. While a particularconfiguration is shown, the biosensor system 500 may have otherconfigurations, including those with additional components.

The test sensor 504 has a base 506 that forms a reservoir 508 and achannel 510 with an opening 512. The reservoir 508 and the channel 510may be covered by a lid with a vent. The reservoir 508 defines apartially-enclosed volume. The reservoir 508 may contain a compositionthat assists in retaining a liquid sample such as water-swellablepolymers or porous polymer matrices. Reagents may be deposited in thereservoir 508 and/or the channel 510. The reagents may include one ormore enzymes, binders, mediators, and like species. The reagents mayinclude a chemical indicator for an optical system. The test sensor 504has a sample interface 514 adjacent to the reservoir 508. The testsensor 504 may have other configurations.

In an optical sensor system, the sample interface 514 has an opticalportal or aperture for viewing the sample. The optical portal may becovered by an essentially transparent material. The sample interface 514may have optical portals on opposite sides of the reservoir 508.

In an electrochemical system, the sample interface 514 has conductorsconnected to a working electrode 532 and a counter electrode 534 fromwhich the analytic output signal may be measured. The sample interface514 also may include conductors connected to one or more additionalelectrodes 536 from which secondary output signals may be measured. Theelectrodes may be substantially in the same plane or in more than oneplane. The electrodes may be disposed on a surface of the base 506 thatforms the reservoir 508. The electrodes may extend or project into thereservoir 508. A dielectric layer may partially cover the conductorsand/or the electrodes. The sample interface 514 may have otherelectrodes and conductors.

The measurement device 502 includes electrical circuitry 516 connectedto a sensor interface 518 and an optional display 520. The electricalcircuitry 516 includes a processor 522 connected to a signal generator524, an optional temperature sensor 526, and a storage medium 528.

The signal generator 524 is capable of providing an electrical inputsignal to the sensor interface 518 in response to the processor 522. Inoptical systems, the electrical input signal may be used to operate orcontrol the detector and light source in the sensor interface 518. Inelectrochemical systems, the electrical input signal may be transmittedby the sensor interface 518 to the sample interface 514 to apply theelectrical input signal to the sample of the biological fluid. Theelectrical input signal may be a potential or current and may beconstant, variable, or a combination thereof, such as when an AC signalis applied with a DC signal offset. The electrical input signal may beapplied continuously or as multiple excitations, sequences, or cycles.The signal generator 524 also may be capable of recording an outputsignal from the sensor interface as a generator-recorder.

The optional temperature sensor 526 is capable of determining theambient temperature of the measurement device 502. The temperature ofthe sample may be estimated from the ambient temperature of themeasurement device 502, calculated from the output signal, or presumedto be the same or similar to the ambient temperature of the measurementdevice 502. The temperature may be measured using a thermister,thermometer, or other temperature sensing device. Other techniques maybe used to determine the sample temperature.

The storage medium 528 may be a magnetic, optical, or semiconductormemory, another storage device, or the like. The storage medium 528 maybe a fixed memory device, a removable memory device, such as a memorycard, remotely accessed, or the like.

The processor 522 is capable of implementing the analyte analysis methodusing computer readable software code and the calibration informationstored in the storage medium 528. The processor 522 may start theanalyte analysis in response to the presence of the test sensor 504 atthe sensor interface 518, the application of a sample to the test sensor504, in response to user input, or the like. The processor 522 iscapable of directing the signal generator 524 to provide the electricalinput signal to the sensor interface 518. The processor 522 is capableof receiving the sample temperature from the temperature sensor 526. Theprocessor 522 is capable of receiving the output signals from the sensorinterface 518.

In electrochemical systems, the analyte responsive primary output signalis generated from the working and counter electrodes 532, 534 inresponse to the reaction of the analyte in the sample. Secondary outputsignals also may be generated from additional electrodes 536. In opticalsystems, the detector or detectors of the sensor interface 518 receivethe primary and any secondary output signals. The output signals may begenerated using an optical system, an electrochemical system, or thelike. The processor 522 is capable of determining analyte concentrationsfrom output signals using the calibration information stored in thestorage medium 528. The results of the analyte analysis may be output tothe display 520, a remote receiver (not shown), and/or may be stored inthe storage medium 528.

The calibration information relating reference sample analyteconcentrations and output signals from the measurement device 502 may berepresented graphically, mathematically, a combination thereof, or thelike. The calibration information is preferably represented ascorrelation equations, which may be represented by a program number(PNA) table, another look-up table, or the like that is stored in thestorage medium 528.

Instructions regarding implementation of the analyte analysis also maybe provided by the computer readable software code stored in the storagemedium 528. The code may be object code or any other code describing orcontrolling the described functionality. The data from the analyteanalysis may be subjected to one or more data treatments, including thedetermination of decay rates, K constants, ratios, functions, and thelike in the processor 522.

In electrochemical systems, the sensor interface 518 has contacts thatconnect or electrically communicate with the conductors in the sampleinterface 514 of the test sensor 504. The sensor interface 518 iscapable of transmitting the electrical input signal from the signalgenerator 524 through the contacts to the connectors in the sampleinterface 514. The sensor interface 518 also is capable of transmittingthe output signal from the sample through the contacts to the processor522 and/or signal generator 524.

In light-absorption and light-generated optical systems, the sensorinterface 518 includes a detector that collects and measures light. Thedetector receives light from the test sensor 504 through the opticalportal in the sample interface 514. In a light-absorption opticalsystem, the sensor interface 518 also includes a light source such as alaser, a light emitting diode, or the like.

The incident beam may have a wavelength selected for absorption by thereaction product. The sensor interface 518 directs an incident beam fromthe light source through the optical portal in the sample interface 514.The detector may be positioned at an angle such as 45° to the opticalportal to receive the light reflected back from the sample. The detectormay be positioned adjacent to an optical portal on the other side of thesample from the light source to receive light transmitted through thesample. The detector may be positioned in another location to receivereflected and/or transmitted light.

The optional display 520 may be analog or digital. The display 520 mayinclude a LCD, a LED, an OLED, a vacuum fluorescent display, or otherdisplay adapted to show a numerical reading. Other display technologiesmay be used. The display 520 electrically communicates with theprocessor 522. The display 520 may be separate from the measurementdevice 502, such as when in wireless communication with the processor522. Alternatively, the display 520 may be removed from the measurementdevice 502, such as when the measurement device 502 electricallycommunicates with a remote computing device, medication dosing pump, andthe like.

In use, a liquid sample for analysis is transferred into the reservoir508 by introducing the liquid to the opening 512. The liquid sampleflows through the channel 510, filling the reservoir 508 while expellingthe previously contained air. The liquid sample chemically reacts withthe reagents deposited in the channel 510 and/or reservoir 508.

The test sensor 502 is disposed in relation to the measurement device502, such that the sample interface 514 is in electrical and/or opticalcommunication with the sensor interface 518. Electrical communicationincludes the transfer of input and/or output signals between contacts inthe sensor interface 518 and conductors in the sample interface 514.Optical communication includes the transfer of light between an opticalportal in the sample interface 514 and a detector in the sensorinterface 518. Optical communication also includes the transfer of lightbetween an optical portal in the sample interface 514 and a light sourcein the sensor interface 518.

The processor 522 is capable of directing the signal generator 524 toprovide an input signal to the sensor interface 518 of the test sensor504. In an optical system, the sensor interface 518 is capable ofoperating the detector and light source in response to the input signal.In an electrochemical system, the sensor interface 518 is capable ofproviding the input signal to the sample through the sample interface514. The test sensor 504 is capable of generating one or more outputsignals in response to the input signal. The processor 522 is capable ofreceiving the output signals generated in response to the redox reactionof the analyte in the sample as previously discussed.

The processor 522 is capable of transforming the output signal using theanalysis method and the calibration information stored in the storagemedium 528 to determine an initial analyte concentration of the sample.The processor 522 may then report this initial analyte concentration asthe final analyte concentration of the sample. Alternatively, theprocessor 522 may further process this initial analyte concentration ofthe sample using a compensation system. More than one compensationand/or other functions also may be implemented by the processor 522.

To provide a clear and more consistent understanding of thespecification and claims of this application, the following definitionsare provided.

“Average” or “Averaged” or “Averaging” includes the combination of twoor more variables to form an average variable. A variable may be anumerical value, an algebraic or scientific expression, or the like. Forexample, averaging may be performed by adding the variables and dividingthe sum by the number of variables; such as in the equationAVG=(a+b+c)/3, where AVG is the average variable and a, b, and c are thevariables. In another example, averaging includes modifying eachvariable by an averaging coefficient and then adding the modifiedvariables to form a weighted average; such as in the equationW_(AVG)=0.2*a+0.4*b+0.4*c, where W_(AVG) is the weighted average, 0.2,0.4 and 0.4 are the averaging coefficients, and a, b, and c are thevariables. The averaging coefficients are numbers between 0 and 1; andif added, will provide a sum of 1 or substantially 1. Other averagingmethods may be used.

“Measurable species” addresses a species the biosensor system isdesigned to determine the presence and/or concentration of in the sampleand may be the analyte of interest or a mediator whose concentration inthe sample is responsive to that of the analyte of interest.

While various embodiments of the invention have been described, it willbe apparent to those of ordinary skill in the art that other embodimentsand implementations are possible within the scope of the invention.

1-77. (canceled)
 78. A method for calibrating a measurement device of abiosensor system, the method comprising: measuring at least two analyteresponsive output signals, the analyte responsive output signals beingaffected by a primary stimulus in the form of a reference sample analyteconcentration from a reference sample, and an extraneous stimulusresulting from one or more of a physical characteristic, anenvironmental aspect, or a manufacturing variation error incorporatedinto the analyte responsive output signals; determining, via ameasurement device, a reference correlation by relating the primarystimulus to the two analyte responsive output signals; measuring, fromthe reference sample, an extraneous stimulus responsive output signal ofthe extraneous stimulus and quantifying the extraneous stimulusresponsive output signal to provide at least two quantified extraneousstimulus values; determining a normalizing relationship from the analyteresponsive output signals and the two quantified extraneous stimulusvalues, the normalizing relationship being determined at a singleselected sample analyte concentration of the reference sample analyteconcentrations; determining a normalized value from the normalizingrelationship by inputting the quantified extraneous stimulus values intothe normalizing relationship; dividing the analyte responsive outputsignals by the normalizing value to provide normalized analyteresponsive output signals; and determining a normalized referencecorrelation between the two normalized analyte responsive output signalsand the reference sample analyte concentrations by a regressiontechnique, the normalized reference correlation providing a reducedstimulus effect in which the only variable between the primary stimulusand the extraneous stimulus is the primary stimulus.
 79. The method ofclaim 78, wherein the measuring of the at least two analyte responsiveoutput signals includes measuring at least four analyte responsiveoutput signals.
 80. The method of claim 78, wherein the measuring of theat least two analyte responsive output signals includes measuring atleast six analyte responsive output signals.
 81. The method of claim 78,wherein the measuring of the at least two analyte responsive outputsignals is concurrent with the measuring of the extraneous stimulusresponsive output signal.
 82. The method of claim 78, wherein thequantifying of the extraneous stimulus responsive output signal isdirect quantifying.
 83. The method of claim 78, wherein the quantifyingof the extraneous stimulus responsive output signal is indirectquantifying.
 84. The method of claim 78, wherein the normalizedreference correlation includes a normalized calibration curve.
 85. Themethod of claim 78, further comprising storing the normalizingrelationship in the measurement device.
 86. The method of claim 78,wherein the sample includes blood.
 87. The method of claim 78, whereinthe extraneous stimulus includes at least one of temperature, totalhemoglobin, or hematocrit.
 88. A biosensor system for calibrating ameasurement device, the system comprising: a test sensor having a sampleinterface adjacent to a reservoir formed by a base, the test sensorbeing configured to generate at least one output signal from a sample;and a measurement device having a processor connected to a sensorinterface, the sensor interface being in electrical communication withthe sample interface, the processor being in electrical communicationwith a storage medium and being configured to measure at least twoanalyte responsive output signals, the analyte responsive output signalsbeing affected by a primary stimulus in the form of a reference sampleanalyte concentration from a reference sample, and an extraneousstimulus resulting from one or more of a physical characteristic, anenvironmental aspect, or a manufacturing variation error incorporatedinto the analyte responsive output signals, determine a referencecorrelation by relating the primary stimulus to the two analyteresponsive output signals, measure, from the reference sample, anextraneous stimulus responsive output signal of the extraneous stimulusand quantifying the extraneous stimulus responsive output signal toprovide at least two quantified extraneous stimulus values, determine anormalizing relationship from the analyte responsive output signals andthe two quantified extraneous stimulus values, the normalizingrelationship being determined at a single selected sample analyteconcentration of the reference sample analyte concentrations, determinea normalized value from the normalizing relationship by inputting thequantified extraneous stimulus values into the normalizing relationship,divide the analyte responsive output signals by the normalizing value toprovide normalized analyte responsive output signals, and determine anormalized reference correlation between the two normalized analyteresponsive output signals and the reference sample analyteconcentrations by a regression technique, the normalized referencecorrelation providing a reduced stimulus effect in which the onlyvariable between the primary stimulus and the extraneous stimulus is theprimary stimulus.
 89. The system of claim 88, wherein the measuring ofthe at least two analyte responsive output signals is concurrent withthe measuring of the extraneous stimulus responsive output signal. 90.The system of claim 88, wherein the normalized reference correlationincludes a normalized calibration curve.
 91. The system of claim 88,wherein the normalizing relationship is stored in the storage medium.92. The system of claim 88, wherein the sample includes blood.